Improving recommendation lists through topic diversification

Ziegler, Cai-Nicolas; McNee, Sean M.; Konstan, Joseph A.; Lausen, Georg

doi:10.1145/1060745.1060754

Cited by 1,435 publications

(976 citation statements)

References 30 publications

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“…For instance, users might become dissatisfied with accurate recommendations when they have no trust in the recommender system's operator [342], their privacy is not ensured [300], they need to wait too long for recommendations [300], or they find the user interfaces unappealing [343]. Other factors that affect user satisfaction are confidence in a recommender system [263], data security [344], diversity [345], user tasks [87], item's lifespan [346] and novelty [347], risk of accepting recommendations [348], robustness against spam and fraud [349], transparency and explanations [350], time to first recommendation [225], and interoperability [351].…”

Section: Focus On Accuracymentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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In the last 16 years, more than 200 research articles were published about research-paper recommender systems. We reviewed these articles and present some descriptive statistics in this paper, as well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users' information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of contentbased and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and Linnaeus University, Kalmar, Sweden sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspect...

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Section: Focus On Accuracymentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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“…Celma and Herrera [4] report an experiment that studied how users judged novel recommendations provided by a CF and a CBF algorithm in the music recommendation context. Ziegler et al [34] and Zhang et al [32] propose diversity as a quality attribute: recommender algorithms should seek to provide optimal coverage of the entire range of user's interests. This work is an example of a combined use of automatic and user-centric quality assessment techniques.…”

Section: Evaluation Of Recommender Systemsmentioning

confidence: 99%

User-Centric vs. System-Centric Evaluation of Recommender Systems

Cremonesi

Garzotto

Turrin³

2013

Lecture Notes in Computer Science

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Abstract. Recommender Systems (RSs) aim at helping users search large amounts of contents and identify more effectively the items (products or services) that are likely to be more useful or attractive. The quality of a RS can be defined from two perspectives: system-centric, in which quality measures (e.g., precision, recall) are evaluated using vast datasets of preferences and opinions on items previously collected from users that are not interacting with the RS under study; user-centric, in which user measures are collected from users interacting with the RS under study. Prior research in e-commerce has provided some empirical evidence that system-centric and user-centric quality methods may lead to inconsistent results, e.g., RSs that were "best" according to systemcentric measures were not the top ones according to user-centric measures. The paper investigates if a similar mismatch also exists in the domain of e-tourism. We discuss two studies that have adopted a system-centric approach using data from 210000 users, and a user-centric approach involving 240 users interacting with an online hotel booking service. In both studies, we considered four RSs that employ an implicit user preference elicitation technique and different baseline and state-of-the-art recommendation algorithms. In these four experimental conditions, we compared system-centric quality measures against user-centric evaluation results. System-centric quality measures were consistent with usercentric measures, in contrast with past studies in e-commerce. This pinpoints that the relationship between the two kinds of metrics may depend on the business sector, is more complex that we may expect, and is a challenging issues that deserves further research.

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“…This approach cannot readily be used for tail queries since it requires user feedback. Clarke et al [6] studied diversification in question answering while Ziegler et al [23] studied the problem from a "recommendation" point of view.…”

Section: Introductionmentioning

confidence: 99%

mNIR: Diversifying Search Results Based on a Mixture of Novelty, Intention and Relevance

Hemayati

Dehkordi

Meng

2012

Web Information Systems Engineering - WISE 2012

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ABSTRACT. Current search engines do not explicitly take different meanings and usages of user queries into consideration when they rank the search results. As a result, they tend to retrieve results that cover the most popular meanings or usages of the query. Consequently, users who want results that cover a rare meaning or usage of query or results that cover all different meanings/usages may have to go through a large number of results in order to find the desired ones. Another problem with current search engines is that they do not adequately take users' intention into consideration. In this paper, we introduce a novel result ranking algorithm (mNIR) that explicitly takes result novelty, user intention-based distribution and result relevancy into consideration and mixes them to achieve better result ranking. We analyze how giving different emphasis to the above three aspects would impact the overall ranking of the results. Our approach builds on our previous method for identifying and ranking possible categories of any user query based on the meanings and usages of the terms and phrases within the query. These categories are also used to generate category queries for retrieving results matching different meanings/usages of the original user query. Our experimental results show that the proposed algorithm can outperform state-of-the-art diversification approaches.

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Improving recommendation lists through topic diversification

Cited by 1,435 publications

References 30 publications

Research-paper recommender systems: a literature survey

Research-paper recommender systems: a literature survey

User-Centric vs. System-Centric Evaluation of Recommender Systems

mNIR: Diversifying Search Results Based on a Mixture of Novelty, Intention and Relevance

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