This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

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Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

Recommender systems provide users with personalized suggestions for products or services. These systems often rely on Collaborating Filtering (CF), where past transactions are analyzed in order to establish connections between users and products. The two more successful approaches to CF are latent factor models, which directly profile both users and products, and neighborhood models, which analyze similarities between products or users. In this work we introduce some innovations to both approaches. The factor and neighborhood models can now be smoothly merged, thereby building a more accurate combined model. Further accuracy improvements are achieved by extending the models to exploit both explicit and implicit feedback by the users. The methods are tested on the Netflix data. Results are better than those previously published on that dataset. In addition, we suggest a new evaluation metric, which highlights the differences among methods, based on their performance at a top-K recommendation task.

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Factorization meets the neighborhood: a multifaceted collaborative filtering model

As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, modelbased, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.

/pdf/a-survey-of-collaborative-filtering-techniques-19zxbumsgq.pdf

A survey of collaborative filtering techniques

A common task of recommender systems is to improve customer experience through personalized recommendations based on prior implicit feedback. These systems passively track different sorts of user behavior, such as purchase history, watching habits and browsing activity, in order to model user preferences. Unlike the much more extensively researched explicit feedback, we do not have any direct input from the users regarding their preferences. In particular, we lack substantial evidence on which products consumer dislike. In this work we identify unique properties of implicit feedback datasets. We propose treating the data as indication of positive and negative preference associated with vastly varying confidence levels. This leads to a factor model which is especially tailored for implicit feedback recommenders. We also suggest a scalable optimization procedure, which scales linearly with the data size. The algorithm is used successfully within a recommender system for television shows. It compares favorably with well tuned implementations of other known methods. In addition, we offer a novel way to give explanations to recommendations given by this factor model.

/pdf/collaborative-filtering-for-implicit-feedback-datasets-4g6rbkxcjl.pdf

Collaborative Filtering for Implicit Feedback Datasets

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field.

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From frequency to meaning: vector space models of semantics

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.

Industry Report: Amazon.com Recommendations: Item-to-Item Collaborative Filtering.

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

Amazon is well-known for personalization and recommendations, which help customers discover items they might otherwise not have found. In this update to their original paper, the authors discuss some of the changes as Amazon has grown.

/pdf/two-decades-of-recommender-systems-at-amazon-com-3q7ahl8aiw.pdf

Two Decades of Recommender Systems at Amazon.com

This paper presents the candidate/critique model of interactive problem solving, in which an automated problem solver communicates candidate solutions to the user and the user critiques those solutions. The system starts with minimal information about the user’s preferences, and preferences are elicited and inferred incrementally by analyzing the critiques. The system’s goal is to present “good” candidates to the user, but to do so it must learn as much as possible about his preferences in order to improve its choice of candidates in subsequent iterations. This system contrasts with traditional decision-analytic and planning frameworks in which a complete model is elicited beforehand or is constructed by a human expert. The paper presents the Automated Travel Assistant, an implemented prototype of the model that interactively builds flight itineraries using realtime airline information. The ATA is available on the World Wide Web and has had over 4000 users between May and October 1996.

Interactive Assessment of User Preference Models: The Automated Travel Assistant

A search tool provides a means of finding a set of items in a large collection of items using a search query. Personalized search generates different search results to different users of the search engine based on their interests and past behavior. The invention describes a method of providing personalized search using previous search queries of the user, pages viewed from previous search results, and the pages viewed by other users with similar searches.

Greg Linden

Papers

Industry Report: Amazon.com Recommendations: Item-to-Item Collaborative Filtering.

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

Two Decades of Recommender Systems at Amazon.com

Interactive Assessment of User Preference Models: The Automated Travel Assistant

Method for personalized search