Collaborative filtering is regarded as one of the most promising recommendation algorithms. The item-based approaches for collaborative filtering identify the similarity between two items by comparing users' ratings on them. In these approaches, ratings produced at different times are weighted equally. That is to say, changes in user purchase interest are not taken into consideration. For example, an item that was rated recently by a user should have a bigger impact on the prediction of future user behaviour than an item that was rated a long time ago. In this paper, we present a novel algorithm to compute the time weights for different items in a manner that will assign a decreasing weight to old data. More specifically, the users' purchase habits vary. Even the same user has quite different attitudes towards different items. Our proposed algorithm uses clustering to discriminate between different kinds of items. To each item cluster, we trace each user's purchase interest change and introduce a personalized decay factor according to the user own purchase behaviour. Empirical studies have shown that our new algorithm substantially improves the precision of item-based collaborative filtering without introducing higher order computational complexity.

Time weight collaborative filtering

There has been recent interests in studying the "goal" behind a user's Web query, so that this goal can be used to improve the quality of a search engine's results. Previous studies have mainly focused on using manual query-log investigation to identify Web query goals. In this paper we study whether and how we can automate this goal-identification process. We first present our results from a human subject study that strongly indicate the feasibility of automatic query-goal identification. We then propose two types of features for the goal-identification task: user-click behavior and anchor-link distribution. Our experimental evaluation shows that by combining these features we can correctly identify the goals for 90% of the queries studied.

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Automatic identification of user goals in Web search

In this paper, we define and present a comprehensive classification of user intent for Web searching. The classification consists of three hierarchical levels of informational, navigational, and transactional intent. After deriving attributes of each, we then developed a software application that automatically classified queries using a Web search engine log of over a million and a half queries submitted by several hundred thousand users. Our findings show that more than 80% of Web queries are informational in nature, with about 10% each being navigational and transactional. In order to validate the accuracy of our algorithm, we manually coded 400 queries and compared the results from this manual classification to the results determined by the automated method. This comparison showed that the automatic classification has an accuracy of 74%. Of the remaining 25% of the queries, the user intent is vague or multi-faceted, pointing to the need for probabilistic classification. We discuss how search engines can use knowledge of user intent to provide more targeted and relevant results in Web searching.

Determining the informational, navigational and transactional intent of web queries

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Determining the informational, navigational, and transactional intent of Web queries

The technique of collaborative filtering is especially successful in generating personalized recommendations. More than a decade of research has resulted in numerous algorithms, although no comparison of the different strategies has been made. In fact, a universally accepted way of evaluating a collaborative filtering algorithm does not exist yet. In this work, we compare different techniques found in the literature, and we study the characteristics of each one, highlighting their principal strengths and weaknesses. Several experiments have been performed, using the most popular metrics and algorithms. Moreover, two new metrics designed to measure the precision on good items have been proposed.The results have revealed the weaknesses of many algorithms in extracting information from user profiles especially under sparsity conditions. We have also confirmed the good results of SVD-based techniques already reported by other authors. As an alternative, we present a new approach based on the interpretation of the tendencies or differences between users and items. Despite its extraordinary simplicity, in our experiments, it obtained noticeably better results than more complex algorithms. In fact, in the cases analyzed, its results are at least equivalent to those of the best approaches studied. Under sparsity conditions, there is more than a 20p improvement in accuracy over the traditional user-based algorithms, while maintaining over 90p coverage. Moreover, it is much more efficient computationally than any other algorithm, making it especially adequate for large amounts of data.

Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems

The identification of the user’s intention or interest through queries that they submit to a search engine can be very useful to offer them more adequate results. In this work we present a framework for the identification of user’s interest in an automatic way, based on the analysis of query logs. This identification is made from two perspectives, the objectives or goals of a user and the categories in which these aims are situated. A manual classification of the queries was made in order to have a reference point and then we applied supervised and unsupervised learning techniques. The results obtained show that for a considerable amount of cases supervised learning is a good option, however through unsupervised learning we found relationships between users and behaviors that are not easy to detect just taking the query words. Also, through unsupervised learning we established that there are categories that we are not able to determine in contrast with other classes that were not considered but naturally appear after the clustering process. This allowed us to establish that the combination of supervised and unsupervised learning is a good alternative to find user’s goals. From supervised learning we can identify the user interest given certain established goals and categories; on the other hand, with unsupervised learning we can validate the goals and categories used, refine them and select the most appropriate to the user’s needs.

The intention behind web queries

In this paper we report results for automatic classification of queries in a wide set of facets that are useful to the identification of query intent. Our hypothesis is that the performance of single-faceted classification of queries can be improved by introducing information of multi-faceted training samples into the learning process. We test our hypothesis by performing a multi-faceted classification of queries based on the combination of correlated facets. Our experimental results show that this idea can significantly improve the quality of the classification. Since most of previous works in query intent classification are oriented to the study of single facets, these results are a first step to an integrated query intent classification model.

A multi-faceted approach to query intent classification

The basic objective of a predictive algorithm for collaborative filtering (CF) is to suggest items to a particular user based on his/her preferences and other users with similar interests. Many algorithms have been proposed for CF, and some works comparing sub-sets of them can be found in the literature; however, more comprehensive comparisons are not available. In this work, a meaningful sample of CF algorithms widely reported in the literature were chosen for analysis; they represent different stages in the evolutive process of CF, starting from simple user correlations, going through online learning, up to methods which use classification techniques. Our main purpose is to compare these algorithms when applied on multi-valued ratings.Experiments were conducted on three well-known datasets with different characteristics, using two protocols and four evaluation metrics, representing coverage, accuracy, reliability and agreement of predictions with respect to real values. Results from such experiments showed that the memory-based method is a good option because its results are more precise and reliable compared with the other methods. Online Learning methods exhibit a good level of accuracy with low variation, which makes them reliable models. On the other hand, Support Vector Machines generate predictions with acceptable agreement; however, their accuracy depends on the characteristics of the input data. Finally, Dependency Networks did not offer good results when applied on multi-valued rankings. The run experiments confirm that the characteristics of datasets keep being an important factor in the performance of methods.

A comparison of several predictive algorithms for collaborative filtering on multi-valued ratings

The amount of information currently available, the different media and presentation formats joined with the little time availability of the researchers and people in general, make necessary the implementation of automated tools selecting and evaluating information, aiming at not only optimizing resources but also obtaining useful and personalized results that optimize the daily work of its users. A technique known as information filtering could be seen as a solution to this problem. Within an information filtering system, a user introduces a profile in the system which represents his/her information needs; then, the system works to display the relevant information. We contain a sample of the research carried out in this important area, focusing the work towards two most representative techniques: "content based filtering" and "collaborative filtering". These techniques have been studied from different points of view, allowing to create a solid framework which involves the necessary criteria for designing and creating a tool using the most outstanding characteristics of each technique. They provide a view to facilitate the work of people devoted to the search, depuration and distribution of information.

http://www.cwr.cl/la-web/2003/stamped/33_perez_j.pdf

Towards an information filtering system in the Web integrating collaborative and content based techniques

The basic objective of a Collaborative Filtering (CF) algorithm is to suggest items to a particular user based on his/her preferences and users with similar interests. Although, there is an apparently strong demand for CF techniques, and many algorithms have been recently proposed, very few articles comparing these techniques can be found. Our paper is oriented towards the study of a sample of algorithms to representing differents stages in the evolutive process of CF.Experiments were conducted on two datasets with different characteristics, using two protocols and three evaluation metrics for the different algorithms. The results indicate that, in general, the Online-Learning (WMA, MWM) and the Support Vector Machines algorithms have a better performance that the other algorithms, on both datasets. Considering the amount of information, the less sparse such information is, the higher the coverage and accuracy of general models tend to be; however, the behavior under sparse data is closer to what is observed in a real system if we have in mind that users usually rate an amount of records much smaller than the total available.

Cristina N. González-Caro

Papers

The intention behind web queries

A multi-faceted approach to query intent classification

A comparison of several predictive algorithms for collaborative filtering on multi-valued ratings

Towards an information filtering system in the Web integrating collaborative and content based techniques

Towards a More Comprehensive Comparison of Collaborative Filtering Algorithms