Item-based relevance modelling of recommendations for getting rid of long tail products

Valcarce, Daniel; Parapar, Javier; Barreiro, Álvaro

doi:10.1016/j.knosys.2016.03.021

Cited by 32 publications

(10 citation statements)

References 14 publications

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“…2. Item-based approach: Item-based method mainly focus on the relationships between papers rather than users [55], [56]. In the item-based approach, there is the assumption that user's interest is continuous or very little change in the future.…”

Section: B Collaborative Filtering (Cf)mentioning

confidence: 99%

Scientific Paper Recommendation: A Survey

et al. 2019

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Globally, the recommendation services have become important due to the fact that they support e-commerce applications and different research communities. Recommender systems have a large number of applications in many fields, including economic, education, and scientific research. Different empirical studies have shown that the recommender systems are more effective and reliable than the keyword-based search engines for extracting useful knowledge from massive amounts of data. The problem of recommending similar scientific articles in scientific community is called scientific paper recommendation. Scientific paper recommendation aims to recommend new articles or classical articles that match researchers' interests. It has become an attractive area of study since the number of scholarly papers increases exponentially. In this paper, we first introduce the importance and advantages of the paper recommender systems. Second, we review the recommendation algorithms and methods, such as Content-based, collaborative filtering, graph-based, and hybrid methods. Then, we introduce the evaluation methods of different recommender systems. Finally, we summarize the open issues in the paper recommender systems, including cold start, sparsity, scalability, privacy, serendipity, and unified scholarly data standards. The purpose of this survey is to provide comprehensive reviews on the scholarly paper recommendation.

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Section: B Collaborative Filtering (Cf)mentioning

confidence: 99%

Scientific Paper Recommendation: A Survey

et al. 2019

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“…Several attempts have been made to diversify the recommendation list while maintaining adequate relevance to the user's preference. Works such as [16,21,33,34] have focused on the business side alone and aimed to increase the aggregate diversity and/or suggest long tail items. This is another reason (apart from user's well being) to improve diversity from the business perspective.…”

Section: Diversity-accuracy Tradeoff In Recommender Systemsmentioning

confidence: 99%

Balancing accuracy and diversity in recommendations using matrix completion framework

Gogna

Majumdar

2017

Knowledge-Based Systems

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Design of recommender systems aimed at achieving high prediction accuracy is a widely researched area. However, several studies have suggested the need for diversified recommendations, with acceptable level of accuracy, to avoid monotony and improve customers' experience. However, increasing diversity comes with an associated reduction in recommendation accuracy; thereby necessitating an optimum tradeoff between the two. In this work, we attempt to achieve accuracy-diversity balance, by exploiting available ratings and item metadata, through a single (joint) optimization model built over the matrix completion framework. Most existing works, unlike our formulation, propose a 2-stage model -a heuristic item ranking scheme on top of an existing collaborative filtering technique. Experimental evaluation on a movie recommender system indicates that our model achieves higher diversity for a given drop in accuracy as compared to existing state of the art techniques.

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“…Nevertheless, other properties are also important, such as diversity and novelty [8,13]. Diversity is the ability of the system to make recommendations that include items equitably from the whole catalog, which is usually desired by vendors [5,22]. On the other hand, novelty is the capacity of the system to produce unexpected recommendations.…”

Section: Introductionmentioning

confidence: 99%

Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings

Landin

Parapar

Barreiro

2020

Lecture Notes in Computer Science

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Nowadays, item recommendation is an increasing concern for many companies. Users tend to be more reactive than proactive for solving information needs. Recommendation accuracy became the most studied aspect of the quality of the suggestions. However, novel and diverse suggestions also contribute to user satisfaction. Unfortunately, it is common to harm those two aspects when optimizing recommendation accuracy. In this paper, we present EER, a linear model for the top-N recommendation task, which takes advantage of user and item embeddings for improving novelty and diversity without harming accuracy.

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Item-based relevance modelling of recommendations for getting rid of long tail products

Cited by 32 publications

References 14 publications

Scientific Paper Recommendation: A Survey

Scientific Paper Recommendation: A Survey

Balancing accuracy and diversity in recommendations using matrix completion framework

Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings

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