An investigation on the serendipity problem in recommender systems

Gemmis, Marco de; Lops, Pasquale; Semeraro, Giovanni; Musto, Cataldo

doi:10.1016/j.ipm.2015.06.008

Cited by 105 publications

(60 citation statements)

References 60 publications

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“…For example, Lu et al [11], Onuma et al [22] and Gemmis [27] are some works that try to propose algorithms for serendipity. Additionally, Kotkov et al [20,21] made a large survey on serendipity.…”

Section: Serendipitymentioning

confidence: 99%

How good your recommender system is? A survey on evaluations in recommendation

Silveira

Zhang

Lin

et al. 2017

Int. J. Mach. Learn. & Cyber.

180

103

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Recommender Systems have become a very useful tool for a large variety of domains. Researchers have been attempting to improve their algorithms in order to issue better predictions to the users. However, one of the current challenges in the area refers to how to properly evaluate the predictions generated by a recommender system. In the extent of offline evaluations, some traditional concepts of evaluation have been explored, such as accuracy, Root Mean Square Error and P@N for top-k recommendations. In recent years, more research have proposed some new concepts such as novelty, diversity and serendipity. These concepts have been addressed with the goal to satisfy the users' requirements. Numerous definitions and metrics have been proposed in previous work. On the absence of a specific summarization on evaluations of recommendation combining traditional metrics and recent progresses, this paper surveys and organizes the main research that present definitions about concepts and propose metrics or strategies to evaluate recommendations. In addition, this survey also settles the relationship between the concepts, categorizes them according to their objectives and suggests potential future topics on user satisfaction.

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Section: Serendipitymentioning

confidence: 99%

How good your recommender system is? A survey on evaluations in recommendation

Silveira

Zhang

Lin

et al. 2017

Int. J. Mach. Learn. & Cyber.

180

103

View full text Add to dashboard Cite

show abstract

“…The algorithm calculates relevance scores using random walks with restarts and bridging scores based on the calculated relevance scores [24]. Another example of an algorithm that belongs to the category of novel algorithms is random walk with restarts enhanced with knowledge infusion [8]. The algorithm orders items in recommendation lists according their relatedness to a user profile.…”

Section: Improving Serendipitymentioning

confidence: 99%

How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm

2018

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Most recommender systems suggest items that are popular among all users and similar to items a user usually consumes. As a result, the user receives recommendations that she/he is already familiar with or would find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected i.e., serendipitous items. In this paper, we propose a serendipity-oriented, reranking algorithm called a serendipity-oriented greedy (SOG) algorithm, which improves serendipity of recommendations through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm, we employed the only publicly available dataset containing user feedback regarding serendipity. We compared our SOG algorithm with topic diversification, popularity baseline, singular value decomposition, serendipitous personalized ranking and Zheng's algorithms relying on the above dataset. SOG outperforms other algorithms in terms of serendipity and diversity. It also outperforms serendipity-oriented algorithms in terms of accuracy, but underperforms accuracy-oriented algorithms in terms of accuracy. We found that the increase of diversity can hurt accuracy and harm or improve serendipity depending on the size of diversity increase.

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“…Section 4.3.3), it sheds light on devising more efficient algorithms for solving ILP formulation of implicit SRD and is less computationally expensive than ILP4ID. Since problems analogous to implicit SRD arise in a variety of applications, e.g., recommender systems [62,63], we believe that our approaches provide a new perspective for addressing problems of this kind.…”

Section: Discussionmentioning

confidence: 99%