Showing papers on "Recommender system published in 2003"

Amazon.com recommendations: item-to-item collaborative filtering

Abstract: Recommendation algorithms are best known for their use on e-commerce Web sites, where they use input about a customer's interests to generate a list of recommended items. Many applications use only the items that customers purchase and explicitly rate to represent their interests, but they can also use other attributes, including items viewed, demographic data, subject interests, and favorite artists. At Amazon.com, we use recommendation algorithms to personalize the online store for each customer. The store radically changes based on customer interests, showing programming titles to a software engineer and baby toys to a new mother. There are three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods. Here, we compare these methods with our algorithm, which we call item-to-item collaborative filtering. Unlike traditional collaborative filtering, our algorithm's online computation scales independently of the number of customers and number of items in the product catalog. Our algorithm produces recommendations in real-time, scales to massive data sets, and generates high quality recommendations.

A Taxonomy of Recommender Agents on theInternet

Abstract: Recently, Artificial Intelligence techniques have proved useful in helping users to handle the large amount of information on the Internet. The idea of personalized search engines, intelligent software agents, and recommender systems has been widely accepted among users who require assistance in searching, sorting, classifying, filtering and sharing this vast quantity of information. In this paper, we present a state-of-the-art taxonomy of intelligent recommender agents on the Internet. We have analyzed 37 different systems and their references and have sorted them into a list of 8 basic dimensions. These dimensions are then used to establish a taxonomy under which the systems analyzed are classified. Finally, we conclude this paper with a cross-dimensional analysis with the aim of providing a starting point for researchers to construct their own recommender system.

MovieLens unplugged: experiences with an occasionally connected recommender system

Abstract: Recommender systems have changed the way people shop online. Recommender systems on wireless mobile devices may have the same impact on the way people shop in stores. We present our experience with implementing a recommender system on a PDA that is occasionally connected to the network. This interface helps users of the MovieLens movie recommendation service select movies to rent, buy, or see while away from their computer. The results of a nine month field study show that although there are several challenges to overcome, mobile recommender systems have the potential to provide value to their users today

Is seeing believing?: how recommender system interfaces affect users' opinions

Abstract: Recommender systems use people's opinions about items in an information domain to help people choose other items. These systems have succeeded in domains as diverse as movies, news articles, Web pages, and wines. The psychological literature on conformity suggests that in the course of helping people make choices, these systems probably affect users' opinions of the items. If opinions are influenced by recommendations, they might be less valuable for making recommendations for other users. Further, manipulators who seek to make the system generate artificially high or low recommendations might benefit if their efforts influence users to change the opinions they contribute to the recommender. We study two aspects of recommender system interfaces that may affect users' opinions: the rating scale and the display of predictions at the time users rate items. We find that users rate fairly consistently across rating scales. Users can be manipulated, though, tending to rate toward the prediction the system shows, whether the prediction is accurate or not. However, users can detect systems that manipulate predictions. We discuss how designers of recommender systems might react to these findings.

Collaborative filtering via gaussian probabilistic latent semantic analysis

Abstract: Collaborative filtering aims at learning predictive models of user preferences, interests or behavior from community data, i.e. a database of available user preferences. In this paper, we describe a new model-based algorithm designed for this task, which is based on a generalization of probabilistic latent semantic analysis to continuous-valued response variables. More specifically, we assume that the observed user ratings can be modeled as a mixture of user communities or interest groups, where users may participate probabilistically in one or more groups. Each community is characterized by a Gaussian distribution on the normalized ratings for each item. The normalization of ratings is performed in a user-specific manner to account for variations in absolute shift and variance of ratings. Experiments on the EachMovie data set show that the proposed approach compares favorably with other collaborative filtering techniques.

Flexible mixture model for collaborative filtering

Abstract: This paper presents a flexible mixture model (FMM) for collaborative filtering. FMM extends existing partitioning/clustering algorithms for collaborative filtering by clustering both users and items together simultaneously without assuming that each user and item should only belong to a single cluster. Furthermore, with the introduction of 'preference' nodes, the proposed framework is able to explicitly model how users rate items, which can vary dramatically, even among the users with similar tastes on items. Empirical study over two datasets of movie ratings has shown that our new algorithm outperforms five other collaborative filtering algorithms substantially.

Mining customer product ratings for personalized marketing

Abstract: With the increasing popularity of Internet commerce, a wealth of information about the customers can now be readily acquired on-line. An important example is the customers' preference ratings for the various products offered by the company. Successful mining of these ratings can thus allow the company's direct marketing campaigns to provide automatic product recommendations. In general, these recommender systems are based on two complementary techniques. Content-based systems match customer interests with information about the products, while collaborative systems utilize preference ratings from the other customers. In this paper, we address some issues faced by these systems, and study how recent machine learning algorithms, namely the support vector machine and the latent class model, can be used to alleviate these problems.

Fast online SVD revisions for lightweight recommender systems

Abstract: The singular value decomposition (SVD) is fundamental to many data modeling/mining algorithms, but SVD algorithms typically have quadratic complexity and require random access to complete data sets. This is problematic in most data mining settings. We detail a family of sequential update rules for adding data to a “thin” SVD data model, revising or removing data already incorporated into the model, and adjusting the model when the data-generating process exhibits nonstationarity. We also leverage the SVD to estimate the most probable completion of incomplete data. We use these methods to model data streams describing tables of consumer/product ratings, where fragments of rows and columns arrive in random order and individual table entries are arbitrarily added, revised, or retracted at any time. These purely online rules have very low time complexity and require a data stream cache no larger than a single user’s ratings. We demonstrate this scheme in an interactive graphical movie recommender that predicts and displays ratings/rankings of thousands of movie titles in real-time as a user adjusts ratings of a small arbitrary set of probe movies. The system “learns” as it is used by revising the SVD in response to user ratings. Users can asynchronously join, add ratings, add movies, revise ratings, get recommendations, and delete themselves from the model.

Nantonac collaborative filtering: recommendation based on order responses

Abstract: A recommender system suggests the items expected to be preferred by the users. Recommender systems use collaborative filtering to recommend items by summarizing the preferences of people who have tendencies similar to the user preference. Traditionally, the degree of preference is represented by a scale, for example, one that ranges from one to five. This type of measuring technique is called the semantic differential (SD) method. Web adopted the ranking method, however, rather than the SD method, since the SD method is intrinsically not suited for representing individual preferences. In the ranking method, the preferences are represented by orders, which are sorted item sequences according to the users' preferences. We here propose some methods to recommed items based on these order responses, and carry out the comparison experiments of these methods.

The power of suggestion

Abstract: User feedback is vital in many recommender systems to help guide the search for good recommendations. Preference-based feedback (e.g. "Show me more like item A") is an inherently ambiguous form of feedback with a limited ability to guide the recommendation process, and for this reason it is usually avoided. Nevertheless we believe that certain domains demand the use of preference-based feedback. As such, we describe and evaluate a flexible recommendation strategy that has the potential to improve the performance of case-based recommenders that rely on preference-based feedback.

The collaborative filtering recommendation based on SOM cluster-indexing CBR

Abstract: Collaborative filtering (CF) recommendation is a knowledge sharing technology for distribution of opinions and facilitating contacts in network society between people with similar interests. The main concerns of the CF algorithm are about prediction accuracy, speed of response time, problem of data sparsity, and scalability. In general, the efforts of improving prediction algorithms and lessening response time are decoupled. We propose a three-step CF recommendation model, which is composed of profiling, inferring, and predicting steps while considering prediction accuracy and computing speed simultaneously. This model combines a CF algorithm with two machine learning processes, Self-Organizing Map (SOM) and Case Based Reasoning (CBR) by changing an unsupervized clustering problem into a supervized user preference reasoning problem, which is a novel approach for the CF recommendation field. This paper demonstrates the utility of the CF recommendation based on SOM cluster-indexing CBR with validation against control algorithms through an open dataset of user preference.

On the role of diversity in conversational recommender systems

Abstract: In the past conversational recommender systems have adopted a similarity-based approach to recommendation, preferring cases that are similar to some user query or profile. Recent research, however, has indicated the importance of diversity as an additional selection constraint. In this paper we attempt to clarify the role of diversity in conversational recommender systems, highlighting the pitfalls of naively incorporating current diversity-enhancing techniques into existing recommender systems. Moreover, we describe and fully evaluate a powerful new diversity-enhancing technique that has the ability to significantly improve the performance of conversational recommender systems across the board.

Clustering approach for hybrid recommender system

Abstract: Recommender system is a kind of Web intelligence techniques to make a daily information filtering for people. Clustering techniques have been applied to the item-based collaborative filtering framework to solve the cold start problem. It also suggests a way to integrate the content information into the collaborative filtering. Extensive experiments have been conducted on MovieLens data to analyze the characteristics of our technique. The results show that our approach contributes to the improvement of prediction quality of the item-based collaborative filtering, especially for the cold start problem.

A Collaborative Filtering Recommendation Algorithm Based on Item Rating Prediction

Abstract: Recommendation system is one of the most important technologies in E-commerce With the development of E-commerce, the magnitudes of users and commodities grow rapidly, resulted in the extreme sparsity of user rating data Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically To address this issue a novel collaborative filtering algorithm based on item rating prediction is proposed This method predicts item ratings that users have not rated by the similarity of items, then uses a new similarity measure to find the target users?neighbors The experimental results show that this method can efficiently improve the extreme sparsity of user rating data, and provid better recommendation results than traditional collaborative filtering algorithms

A recommendation mechanism for contextualized mobile advertising

Abstract: Mobile advertising complements the Internet and interactive television advertising and makes it possible for advertisers to create tailor-made campaigns targeting users according to where they are, their needs of the moment and the devices they are using (ie contextualized mobile advertising) Therefore, it is necessary that a fully personalized mobile advertising infrastructure be made In this paper, we present such a personalized contextualized mobile advertising infrastructure for the advertisement of commercial/non-commercial activities We name this infrastructure MALCR, in which the primary ingredient is a recommendation mechanism that is supported by the following concepts: (1) minimize users' inputs (a typical interaction metaphor for mobile devices) for implicit browsing behaviors to be best utilized; (2) implicit browsing behaviors are then analyzed with a view to understanding the users' interests in the values of features of advertisements; (3) having understood the users' interests, Mobile Ads relevant to a designated location are subsequently scored and ranked; (4) Top-N scored advertisements are recommended The recommendation mechanism is novel in its combination of two-level Neural Network learning, Neural Network sensitivity analysis, and attribute-based filtering This recommendation mechanism is also justified (by thorough evaluations) to show its ability in furnishing effective personalized contextualized mobile advertising

Interfaces for eliciting new user preferences in recommender systems

Abstract: Recommender systems build user models to help users find the items they will find most interesting from among many available items One way to build such a model is to ask the user to rate a selection of items The choice of items selected affects the quality of the user model generated In this paper, we explore the effects of letting the user participate in choosing the items that are used to develop the model We compared three interfaces to elicit information from new users: having the system choose items for users to rate, asking the users to choose items themselves, and a mixed-initiative interface that combines the other two methods We found that the two pure interfaces both produced accurate user models, but that directly asking users for items to rate increases user loyalty in the system Ironically, this increased loyalty comes despite a lengthier signup process The mixed-initiative interface is not a reasonable compromise as it created less accurate user models with no increase in loyalty

Particle swarm optimization recommender system

Abstract: Recommender systems are new types of Internet-based software tools, designed to help users find their way through today's complex on-line shops and entertainment Web sites. This paper describes a new recommender system, which employs a particle swarm optimization (PSO) algorithm to learn personal preferences of users and provide tailored suggestions. Experiments are carried out to observe the performance of the system and results are compared to those obtained from the genetic algorithm (GA) recommender system and a standard, non-adaptive system based on the Pearson algorithm.

Studying Recommendation Algorithms by Graph Analysis

Abstract: We present a novel framework for studying recommendation algorithms in terms of the ‘jumps’ that they make to connect people to artifacts. This approach emphasizes reachability via an algorithm within the implicit graph structure underlying a recommender dataset and allows us to consider questions relating algorithmic parameters to properties of the datasets. For instance, given a particular algorithm ‘jump,’ what is the average path length from a person to an artifact? Or, what choices of minimum ratings and jumps maintain a connected graph? We illustrate the approach with a common jump called the ‘hammock’ using movie recommender datasets.

Case-based recommender systems: a unifying view

Abstract: This paper presents a unifying framework to model case-based reasoning recommender systems (CBR-RSs). CBR-RSs have complex architectures and specialize the CBR problem solving methodology in a number of ways. The goal of the proposed framework is to illustrate both the common features of the various CBR-RSs as well as the points were these systems take different solutions. The proposed framework was derived by the analysis of some systems and techniques comprising nine different recommendation functionalities. The ultimate goal of the this framework is to ease the evaluation and the comparison of case-based reasoning recommender systems and to provide a tool to identify open areas for further research.

RACOFI: A Rule-Applying Collaborative Filtering System

Abstract: In this paper 1 we give an overview of the RACOFI (RuleApplying Collaborative Filtering) multidimensional rating system and its related technologies. This will be exemplified with RACOFI Music, an implemented collaboration agent that assists on-line users in the rating and recommendation of audio (Learning) Objects. It lets users rate contemporary Canadian music in the five dimensions of impression, lyrics, music, originality, and production. The collaborative filtering algorithms STI Pearson, STIN2, and the Per Item Average algorithms are then employed together with RuleML-based rules to recommend music objects that best match user queries. RACOFI has been on-line since August 2003 at [http://racofi.elg.ca].

PILGRIM: A location broker and mobility-aware recommendation system

Abstract: Mobile computing adds a mostly unexplored dimension to data mining: user's position is a relevant piece of information, and recommendation systems, selecting and ranking links of interest to the user, have the opportunity to take location into account. In this paper a mobility-aware recommendation system that considers the location of the user to filter recommended links is proposed. To avoid the potential problems and costs of insertion by hand, a new middleware layer, the location broker, maintains a historic database of locations and corresponding links used in the past and develops models relating resources to their spatial usage pattern. These models are used to calculate a preference metric when the current user is asking for resources of interest. Mobility scenarios are described and analyzed in terms of possible user requirements. The features of the PILGRIM mobile recommendation system are outlined together with a preliminary experimental evaluation of different metrics.

A tourism recommender system based on collaboration and text analysis.

Abstract: This work presents a recommender system that helps travel agents in discovering options for customers, especially those who do not know where to go and what to do. The system analyzes textual messages exchanged between a travel agent and a customer through a private Web chat. Text mining techniques help discover interesting areas in the messages. After that, the system searches a database and retrieves tourist options (like cities and attractions) classified in these interesting areas. The system makes use of a tourism ontology, containing themes and a controlled vocabulary, to identify themes in the textual messages. The system acts as a decision support system, because it does not make recommendations directly to the customer.

Method and system for pushing services to mobile devices in smart environments using a context-aware recommender

Abstract: A context-aware service recommender system receives current user context and recommends a list of browser-based services to a user on a mobile devices. A user's mobile device receives context events from smart environments in which the mobile device is operating. Data about the context events is relayed to a service recommendation server. The server develops recommendations based on the context and other factors, and relays information about the recommended services to the mobile device. As each recommended service is selected or ignored by the user of the mobile device, the device sends implicit feedback with this information to the service recommendation server for use in subsequent recommendations.

Similarity measure and instance selection for collaborative filtering

Abstract: Collaborative filtering has been very successful in both research and applications such as information filtering and E-commerce. The k-Nearest Neighbor (KNN) method is a popular way for its realization. Its key technique is to find k nearest neighbors for a given user to predict his interests. However, this method suffers from two fundamental problems: sparsity and scalability. In this paper, we present our solutions for these two problems. We adopt two techniques: a matrix conversion method for similarity measure and an instance selection method. And then we present an improved collaborative filtering algorithm based on these two methods. In contrast with existing collaborative algorithms, our method shows its satisfactory accuracy and performance.

Capturing interest through inference and visualization: ontological user profiling in recommender systems

Abstract: Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a diverse and dynamic environment. Recommender systems help where explicit search queries are not available or are difficult to formulate, learning the type of thing users like over a period of time.We explore an ontological approach to user profiling in the context of a recommender system. Building on previous work involving ontological profile inference and the use of external ontologies to overcome the cold-start problem, we explore the idea of profile visualization to capture further knowledge about user interests. Our system, called Foxtrot, examines the problem of recommending on-line research papers to academic researchers. Both our ontological approach to user profiling and our visualization of user profiles are novel ideas to recommender systems. A year long experiment is conducted with over 200 staff and students at the University of Southampton. The effectiveness of visualizing profiles and eliciting profile feedback is measured, as is the overall effectiveness of the recommender system.

Towards more conversational and collaborative recommender systems

Abstract: Current recommender systems, based on collaborative filtering, implement a rather limited model of interaction. These systems intelligently elicit information from a user only during the initial registration phase. Furthermore, users tend to collaborate only indirectly. We believe there are several unexplored opportunities in which information can be effectively elicited from users by making the underlying interaction model more conversational and collaborative. In this paper, we propose a set of techniques to intelligently select what information to elicit from the user in situations in which the user may be particularly motivated to provide such information. We argue that the resulting interaction improves the user experience. We conclude by reporting results of an offline experiment in which we compare the influence of different elicitation techniques on both the accuracy of the systems predictions and the users effort

Feature Weighting and Instance Selection for Collaborative Filtering: An Information-Theoretic Approach

Abstract: Collaborative filtering (CF) employing a consumer preference database to make personal product recommendations is achieving widespread success in E-commerce. However, it does not scale well to the ever-growing number of consumers. The quality of the recommendation also needs to be improved in order to gain more trust from consumers. This paper attempts to improve the accuracy and efficiency of collaborative filtering. We present a unified information-theoretic approach to measure the relevance of features and instances. Feature weighting and instance selection methods are proposed for collaborative filtering. The proposed methods are evaluated on the well-known EachMovie data set and the experimental results demonstrate a significant improvement in accuracy and efficiency.

DIETORECS: travel advisory for multiple decision styles.

Abstract: This paper presents Dietorecs, a novel case-based travel planning recommender system. Dietorecs has been designed by incorporating a human decision model that stresses individual differences in decision styles. Dietorecs supports decision styles by means of an adaptive behavior which is learned exploiting a case base of recommendation sessions that are stored by the systems. Users can enter the system through three main functional doors that fit groups of decision styles, but they can eventually switch the type of support required. The dialogue (questions) is personalized using both the user model (cases) and statistics over the data available in the virtual catalogues provided by two DMOs.