PEIA: Personality and Emotion Integrated Attentive Model for Music Recommendation on Social Media Platforms

Abstract

With the rapid expansion of digital music formats, it's indispensable to recommend users with their favorite music. For music recommendation, users' personality and emotion greatly affect their music preference, respectively in a long-term and short-term manner, while rich social media data provides effective feedback on these information. In this paper, aiming at music recommendation on social media platforms, we propose a Personality and Emotion Integrated Attentive model (PEIA), which fully utilizes social media data to comprehensively model users' long-term taste (personality) and short-term preference (emotion). Specifically, it takes full advantage of personality-oriented user features, emotion-oriented user features and music features of multi-faceted attributes. Hierarchical attention is employed to distinguish the important factors when incorporating the latent representations of users' personality and emotion. Extensive experiments on a large real-world dataset of 171,254 users demonstrate the effectiveness of our PEIA model which achieves an NDCG of 0.5369, outperforming the state-of-the-art methods. We also perform detailed parameter analysis and feature contribution analysis, which further verify our scheme and demonstrate the significance of co-modeling of user personality and emotion in music recommendation.

Summarize before Aggregate: A Global-to-local Heterogeneous Graph Inference Network for Conversational Emotion Recognition

“…depression and suicidal intention) in time (Gkotsis et al, 2016;Korkontzelos et al, 2016;. Moreover, emotion recognition can bring benefits to online recommendation on social media platforms by modeling users' short-term preferences (Zhang et al, 2014;Shen et al, 2020). Recently, an increasing number of models have been proposed to solve CER using various structures.…”

“…Conversational Emotion Recognition (CER) has attracted increasing interests for its promising applications in intelligent interactive systems with diverse functionalities, including medical-care systems and online recommendation systems (Zhang et al, 2014;Gkotsis et al, 2016;Shen et al, 2020). As shown in Figure 1, conversations in CER datasets are segmented into multiple utterances based on breaths or pauses of the speaker, and each utterance is associated with an emotion label.…”

A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network

“…Park et al proposed an emotion state representation model by extracting social media data to recommend music [22]. Shen et al proposed a model to represent user emotion state as short-term preference and used data from social media to provide music recommendations based on the model [23]. Other improvements in emotion-aware music recommendations including the context-based method [24], hybrid approaches [25], incorporation of deep learning methods [26], etc.…”

“…However, studies have proved that aside from the six basic emotions, there are more categories of emotions [28], and it is better to model emotion in a continuous way [29]. Secondly, user emotion state extracted from social media data can only represent user emotion state or the music emotion preference of the user at some certain time points, as stated by Shen et al [23] emotion accounts for a short-term preference that drifts across time [30]. While the recommender should generate recommendations whenever the user enters the system, there is still a need for continuous emotion state recognition across time.…”

An approach to the sequential evaluation of emotional behaviors of depressive users on social networks in groups and individually

“…Several studies have been conducted in order to understand user behaviors and social aspects online, such as how connections and bonds of friendship occur (TODD; BLEVINS; YI, 2020;MANNING, 2020;BORAH, 2020;STRAUGHAN;BISSELL;GORMAN-MURRAY, 2021), predict elections (Belcastro et al, 2020;CHAUHAN;SHARMA;SIKKA, 2021;SKORIC;JAIDKA, 2020), make product recommendations (SHEN et al, 2020;TROUDI et al, 2020), among others. In this context, data science approaches have been investigated to extract characteristics, recognize patterns, and make predictions based on the large amount of data produced and shared by users formed by different media, e.g.…”