Analysis of recommendation algorithms for e-commerce

Analysis of recommendation algorithms for e-commerce (2000)

by B M Sarwar, G Karypis, J A Konstan, J Riedl

Venue:

in Proc. of the ACM Conference on Electronic Commerce (EC-00

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Mining the search trails of surfing crowds: identifying relevant websites from user activity

by Mikhail Bilenko - IN: PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB , 2008

"... The paper proposes identifying relevant information sources from the history of combined searching and browsing behavior of many Web users. While it has been previously shown that user interactions with search engines can be employed to improve document ranking, browsing behavior that occurs beyond ..."

Abstract - Cited by 61 (12 self) - Add to MetaCart

The paper proposes identifying relevant information sources from the history of combined searching and browsing behavior of many Web users. While it has been previously shown that user interactions with search engines can be employed to improve document ranking, browsing behavior that occurs beyond search result pages has been largely overlooked in prior work. The paper demonstrates that users’ post-search browsing activity strongly reflects implicit endorsement of visited pages, which allows estimating topical relevance of Web resources by mining large-scale datasets of search trails. We present heuristic and probabilistic algorithms that rely on such datasets for suggesting authoritative websites for search queries. Experimental evaluation shows that exploiting complete post-search browsing trails outperforms alternatives in isolation (e.g., clickthrough logs), and yields accuracy improvements when employed as a feature in learning to rank for Web search.

Conserved network motifs allow protein–protein interaction prediction

by István Albert , Réka Albert , 2004

"... Motivation: High-throughput protein interaction detection methods are strongly affected by false positive and false negative results. Focused experiments are needed to complement the large-scale methods by validating previously detected interactions but it is often difficult to decide which protei ..."

Abstract - Cited by 55 (2 self) - Add to MetaCart

Motivation: High-throughput protein interaction detection methods are strongly affected by false positive and false negative results. Focused experiments are needed to complement the large-scale methods by validating previously detected interactions but it is often difficult to decide which proteins to probe as interaction partners. Developing reliable computational methods assisting this decision process is a pressing need in bioinformatics. Results: We show that we can use the conserved properties of the protein network to identify and validate interaction candidates. We apply a number of machine learning algorithms to the protein connectivity information and achieve a surprisingly good overall performance in predicting interacting proteins. Using a ‘leave-one-out ’ approach we find average success rates between 20 and 40 % for predicting the correct interaction partner of a protein. We demonstrate that the success of these methods is based on the presence of conserved interaction motifs within the network. Availability: A reference implementation and a table with candidate interacting partners for each yeast protein are available

Intelligent Techniques for Web Personalization”

by B Mobasher, S S Anand - in post-proceedings of the Second Workshop on Intelligent Techniques in Web Personalization, , 2005

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Abstract - Cited by 49 (1 self) - Add to MetaCart

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Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques

by Gediminas Adomavicius, Youngok Kwon

"... Abstract — Recommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy (as exemplified by ..."

Abstract - Cited by 48 (1 self) - Add to MetaCart

Abstract — Recommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy (as exemplified by the recent Netflix Prize competition), other important aspects of recommendation quality, such as the diversity of recommendations, have often been overlooked. In this paper, we introduce and explore a number of item ranking techniques that can generate recommendations that have substantially higher aggregate diversity across all users while maintaining comparable levels of recommendation accuracy. Comprehensive empirical evaluation consistently shows the diversity gains of the proposed techniques using several real-world rating datasets and different rating prediction algorithms. Index Terms — Recommender systems, recommendation diversity, ranking functions, performance evaluation metrics, collaborative filtering. 1

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A survey of accuracy evaluation metrics of recommendation tasks

by Asela Gunawardana, Guy Shani, Lyle Ungar - Journal of Machine Learning Research

"... Recommender systems are now popular both commercially and in the research community, where many algorithms have been suggested for providing recommendations. These algorithms typically perform differently in various domains and tasks. Therefore, it is important from the research perspective, as well ..."

Abstract - Cited by 48 (0 self) - Add to MetaCart

Recommender systems are now popular both commercially and in the research community, where many algorithms have been suggested for providing recommendations. These algorithms typically perform differently in various domains and tasks. Therefore, it is important from the research perspective, as well as from a practical view, to be able to decide on an algorithm that matches the domain and the task of interest. The standard way to make such decisions is by comparing a number of algorithms offline using some evaluation metric. Indeed, many evaluation metrics have been suggested for comparing recommendation algorithms. The decision on the proper evaluation metric is often critical, as each metric may favor a different algorithm. In this paper we review the proper construction of offline experiments for deciding on the most appropriate algorithm. We discuss three important tasks of recommender systems, and classify a set of appropriate well known evaluation metrics for each task. We demonstrate how using an improper evaluation metric can lead to the selection of an improper algorithm for the task of interest. We also discuss other important considerations when designing offline experiments.

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A user-centric evaluation framework for recommender systems

by Li Chen, Pearl Pu - In RecSys ’11 , 2011

"... This paper explores the evaluation issues of recommender systems particularly from users ’ perspective. We first show results of literature surveys on human psychological decision theory and trust building in online environments. Based on the results, we propose an evaluation framework aimed at asse ..."

Abstract - Cited by 44 (4 self) - Add to MetaCart

This paper explores the evaluation issues of recommender systems particularly from users ’ perspective. We first show results of literature surveys on human psychological decision theory and trust building in online environments. Based on the results, we propose an evaluation framework aimed at assessing a recommender’s practical ability in providing decision support benefits to end-users from various aspects. It includes both accuracy/effort measures and a user-trust model of subjective constructs, and a corresponding sample questionnaire design. Author Keywords Recommender systems, user evaluation, adaptive decision theory, trust building, decision accuracy and effort. ACM Classification H5.m. Information interfaces and presentation (e.g., HCI): Miscellaneous.

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Recommender systems research: a connection-centric survey

by Saverio Perugini, Marcos André Gonçalves, Edward A. Fox - J. INTELL. INF. SYST , 2004

"... Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and ..."

Abstract - Cited by 42 (2 self) - Add to MetaCart

Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and challenging research area of its own. Recommender systems have traditionally been studied from a content-based filtering vs. collaborative design perspective. Recommendations, however, are not delivered within a vacuum, but rather cast within an informal community of users and social context. Therefore, ultimately all recommender systems make connections among people and thus should be surveyed from such a perspective. This viewpoint is under-emphasized in the recommender systems literature. We therefore take a connection-oriented perspective toward recommender systems research. We posit that recommendation has an inherently social element and is ultimately intended to connect people either directly as a result of explicit user modeling or indirectly through the discovery of relationships implicit in extant data. Thus, recommender systems are characterized by how they model users to bring people together: explicitly or implicitly. Finally, user modeling and the connection-centric viewpoint raise broadening and social issues—such as evaluation, targeting, and privacy and trust—which we also briefly address.

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