Evaluating content novelty in recommender systems

Mendoza, Marcelo; Torres, Nicolás

doi:10.1007/s10844-019-00548-x

Cited by 19 publications

(12 citation statements)

References 23 publications

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“…• Novelty and Diversity. Novelty measures the ability of RS to recommend items that appear novel to the user [89]. Conversely, diversity measures the ability of the RS to recommend items that are not similar to those preferred by the user in the past or that is not limited to recommending popular items only [90,91].…”

Section: Development and Evaluation Levelsmentioning

confidence: 99%

A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions

Chicaiza

Valdiviezo-Díaz

2021

Information

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In recent years, the use of recommender systems has become popular on the web. To improve recommendation performance, usage, and scalability, the research has evolved by producing several generations of recommender systems. There is much literature about it, although most proposals focus on traditional methods’ theories and applications. Recently, knowledge graph-based recommendations have attracted attention in academia and the industry because they can alleviate information sparsity and performance problems. We found only two studies that analyze the recommendation system’s role over graphs, but they focus on specific recommendation methods. This survey attempts to cover a broader analysis from a set of selected papers. In summary, the contributions of this paper are as follows: (1) we explore traditional and more recent developments of filtering methods for a recommender system, (2) we identify and analyze proposals related to knowledge graph-based recommender systems, (3) we present the most relevant contributions using an application domain, and (4) we outline future directions of research in the domain of recommender systems. As the main survey result, we found that the use of knowledge graphs for recommendations is an efficient way to leverage and connect a user’s and an item’s knowledge, thus providing more precise results for users.

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Section: Development and Evaluation Levelsmentioning

confidence: 99%

A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions

Chicaiza

Valdiviezo-Díaz

2021

Information

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“…For every test in the experimental testing results presented in the next section (Figures 6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57), 196 iterations are presented, with the proposed time slices consisting of every 28 iterations. Accordingly, seven sets (phases) of 28 iterations are implemented as time slices, with the percentage of random suggestions…”

Section: Testing Preliminariesmentioning

confidence: 99%

“…0% All experiments (Figures 6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,36,37,38,39,40,41,42,43) that are not related to user selections are offline experiments. The experiments (Figures 30,31,32,33,34,35,44,45,46,47,48,49) that are related to user selections are also offline experiments but with real-time calculation of the evaluation degrees of the selections and updating of the user profiles. The results would not show major changes in the case of online tests because the evaluations are related to selections of each user and her/his reactions to the decisions of the RS.…”

Section: Experiments and Solutionsmentioning

confidence: 99%

“…Developing new concepts of diversity and novelty evaluation, article [ 31 ] uses novelty as a parameter (degree) for guiding diversity variations. The underlying idea of this article is the most similar to that of the current research, in which a human behavior parameter (user selections) is used as a main variable for novelty and diversity evaluations in the context of a new concept of applicability, with the goal of providing better user satisfaction and utility.…”

Section: Literature Reviewmentioning

confidence: 99%

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New recommender system evaluation approaches based on user selections factor

Kshour

Ebrahimi

Goliaee

et al. 2021

Heliyon

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Currently, due to the increasing importance of recommender systems (RSs), especially in the fields of social networking and e-commerce, these systems represent one of the most interesting subjects in computer programming. Although many research reports have previously been published in this subject area, because of lack of clarity regarding their algorithms or limited comparisons with the literature, most of them are difficult to extend for similar applications in the future. Therefore, in the present study, we have attempted to improve two novel RS evaluation measures (variety and newness) developed from previous evaluator rules (namely, diversity and novelty) based on human behavior so as to be more reliable and compatible with various developments in RSs. The new rules provide higher weighting for suggestions and respect for users' behavior and can be used in place of diversity and novelty rules with better precision and centralization, by 22.54% for variety and by 14.84% for newness. In addition, we aim to use the developed measures to improve new RSs and support better comparative analyses in this field in the future. This contribution is expected to facilitate better RS research and competition, especially in the social networking domain.

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“…Pietro Gravino et al introduced the concept of "adjacent possible" to redesign a recommendation system to meet user demands [16]. For users, novelty is a "new" thing and differs from known things, making it a kind of user perception [17]. K. G. Saranya et al used maximum cosine and average cosine distances to measure the distance of new documents to those already known by users for verifying their novelty [18].…”

Section: Jestr Rmentioning

confidence: 99%

A Novel Recommendation Algorithm Based on Diffusion of Innovation Theory

Zhang¹,

Qian²,

Lv³

et al. 2019

JESTR

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Determining whether an item is "novel" for target users is a crucial problem and understanding their preference and awareness degree of the item has been widely investigated in novelty recommendations. A recommendation algorithm based on "diffusion of innovation" theory was proposed in this study to verify the novelty of recommendation results on the precondition of ensuring their accuracy. Items were clustered using the K-means clustering algorithm. Users' positivity of innovation adoption for each item was calculated on the basis of the items adopted by the users, and then the novelty of the items for target users was calculated by combining their popularity change, user preference, and user difference. Results of traditional recommendation algorithms were integrated for recommendation on the basis of fusion strategy results. The effectiveness of the proposed algorithm was verified through an offline experiment. Results indicate that the novelty of the recommendation list of the proposed algorithm is remarkably higher than that of traditional algorithms. The novelty is high when the quantity of alternative sets reaches 400 or more, where the average popularity of the recommendation list declines by 40%, and the coverage is elevated by 100%, thereby improving the ability of the proposed system to extract all kinds of items. This study serves as reference for the improvement of user satisfaction with recommendation systems.

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Evaluating content novelty in recommender systems

Cited by 19 publications

References 23 publications

A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions

A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions

New recommender system evaluation approaches based on user selections factor

A Novel Recommendation Algorithm Based on Diffusion of Innovation Theory

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