Social Influence-Based Similarity Measures for User-User Collaborative Filtering Applied to Music Recommendation

Sánchez-Moreno, Diego; Pérez-Marcos, Javier; González, Ana Belén Gil; Batista, Vivian F. López

doi:10.1007/978-3-319-99608-0_30

Cited by 8 publications

(7 citation statements)

References 11 publications

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“…Another more innovative use of social tagging is the inference of user expertise in order to find more trusted neighbors for CF [28]. Friendship relations between users of streaming platforms is a different type of social information that can be treated jointly with user preferences in order to improve music recommendations [29].…”

Section: Related Workmentioning

confidence: 99%

A Session-Based Song Recommendation Approach Involving User Characterization along the Play Power-Law Distribution

et al. 2020

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In recent years, streaming music platforms have become very popular mainly due to the huge number of songs these systems make available to users. This enormous availability means that recommendation mechanisms that help users to select the music they like need to be incorporated. However, developing reliable recommender systems in the music field involves dealing with many problems, some of which are generic and widely studied in the literature while others are specific to this application domain and are therefore less well-known. This work is focused on two important issues that have not received much attention: managing gray-sheep users and obtaining implicit ratings. The first one is usually addressed by resorting to content information that is often difficult to obtain. The other drawback is related to the sparsity problem that arises when there are obstacles to gather explicit ratings. In this work, the referred shortcomings are addressed by means of a recommendation approach based on the users’ streaming sessions. The method is aimed at managing the well-known power-law probability distribution representing the listening behavior of users. This proposal improves the recommendation reliability of collaborative filtering methods while reducing the complexity of the procedures used so far to deal with the gray-sheep problem.

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Section: Related Workmentioning

confidence: 99%

A Session-Based Song Recommendation Approach Involving User Characterization along the Play Power-Law Distribution

et al. 2020

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“…Users' relationships in a social network are also leveraged by [Sánchez-Moreno et al 2018], who compute measures of social influence of users from their friendship connections. These measures are integrated into a memory-based collaborative filtering algorithm to give more weights to influential users.…”

Section: Accomplishing Context-awarenessmentioning

confidence: 99%

“…• Integrate social influence into user-based CF[Sánchez-Moreno et al 2018] ✓ ✓• Factor graphical model of social influence[Chen et al 2019] ✓ ✓• Audio, cultural, and socio-economic user model[Zangerle et al 2020] ✓ ✓ ✓• Music-cultural country clusters, VAE[Schedl et al 2021a] …”

mentioning

confidence: 99%

Content-driven Music Recommendation: Evolution, State of the Art, and Challenges

Deldjoo¹,

Schedl²,

Knees³

2021

Preprint

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The music domain is among the most important ones for adopting recommender systems technology. In contrast to most other recommendation domains, which predominantly rely on collaborative filtering (CF) techniques, music recommenders have traditionally embraced content-based (CB) approaches. In the past years, music recommendation models that leverage collaborative and content data -which we refer to as content-driven models -have been replacing pure CF or CB models.In this survey, we review 47 articles on content-driven music recommendation. Based on a thorough literature analysis, we first propose an onion model comprising five layers, each of which corresponds to a category of music content we identified: signal, embedded metadata, expert-generated content, usergenerated content, and derivative content. We provide a detailed characterization of each category along several dimensions. Second, we identify six overarching challenges, according to which we organize our main discussion: increasing recommendation diversity and novelty, providing transparency and explanations, accomplishing context-awareness, recommending sequences of music, improving scalability and efficiency, and alleviating cold start. Each article addressing one or more of these challenges is categorized according to the content layers of our onion model, the article's goal(s), and main methodological choices. Furthermore, articles are discussed in temporal order to shed light on the evolution of content-driven music recommendation strategies. Finally, we provide our personal selection of the persisting grand challenges, which are still waiting to be solved in future research endeavors.

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“…However, most of the proposals are hybrid approaches [12] that use the ratings and information from users or items. This additional information can be item content data (e.g., the genre of a movie) [13] or user data such as demographic attributes [14], user social data [15,16], and other types of user information [17] which is usually used in combination with ratings to compute similarity.…”

Section: 𝑐𝑐𝑐𝑐𝑐𝑐 �𝑅𝑅 𝑢𝑢 𝑎𝑎 𝑅𝑅 𝑢𝑢mentioning

confidence: 99%

“…Considering a tridimensional data space 𝐷𝐷 where the ratings in set 𝐷𝐷 depend on users, items and time context, the contextual pre-filtering reduces the space from three to two dimensions by setting the time to a value 𝑡𝑡, as seen in Equation ( 14), or a range of values 𝑐𝑐 𝑡𝑡 , as seen in Equation (15).…”

Section: Modeling Time Contextmentioning

confidence: 99%

Time-Aware Music Recommender Systems: Modeling the Evolution of Implicit User Preferences and User Listening Habits in A Collaborative Filtering Approach

2020

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Online streaming services have become the most popular way of listening to music. The majority of these services are endowed with recommendation mechanisms that help users to discover songs and artists that may interest them from the vast amount of music available. However, many are not reliable as they may not take into account contextual aspects or the ever-evolving user behavior. Therefore, it is necessary to develop systems that consider these aspects. In the field of music, time is one of the most important factors influencing user preferences and managing its effects, and is the motivation behind the work presented in this paper. Here, the temporal information regarding when songs are played is examined. The purpose is to model both the evolution of user preferences in the form of evolving implicit ratings and user listening behavior. In the collaborative filtering method proposed in this work, daily listening habits are captured in order to characterize users and provide them with more reliable recommendations. The results of the validation prove that this approach outperforms other methods in generating both context-aware and context-free recommendations.

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Social Influence-Based Similarity Measures for User-User Collaborative Filtering Applied to Music Recommendation

Cited by 8 publications

References 11 publications

A Session-Based Song Recommendation Approach Involving User Characterization along the Play Power-Law Distribution

A Session-Based Song Recommendation Approach Involving User Characterization along the Play Power-Law Distribution

Content-driven Music Recommendation: Evolution, State of the Art, and Challenges

Time-Aware Music Recommender Systems: Modeling the Evolution of Implicit User Preferences and User Listening Habits in A Collaborative Filtering Approach

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