Using rich social media information for music recommendation via hypergraph model

Tan, Shulong; Bu, Jiajun; Chen, Chun; Xu, Bin; Wang, Can; He, Xiaofei

doi:10.1145/2037676.2037679

Cited by 50 publications

(21 citation statements)

References 38 publications

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“…For example, different sources may have certain elements that are unique or specific to them. This problem has been recently indicated in the context of processing social network data in business applications (Bonchi et al 2011) or in the context of music recommender systems (Tan et al 2011).…”

Section: Modeling Heterogeneous Geosocial Datamentioning

confidence: 99%

Geosocial gauge: a system prototype for knowledge discovery from social media

Croitoru

Crooks

Radzikowski

et al. 2013

International Journal of Geographical Information Science

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The remarkable success of online social media sites marks a shift in the way people connect and share information. Much of this information now contains some form of geographical content because of the proliferation of location-aware devices, thus fostering the emergence of geosocial media -a new type of user-generated geospatial information. Through geosocial media we are able, for the first time, to observe human activities in scales and resolutions that were so far unavailable. Furthermore, the wide spectrum of social media data and service types provides a multitude of perspectives on real-world activities and happenings, thus opening new frontiers in geosocial knowledge discovery. However, gleaning knowledge from geosocial media is a challenging task, as they tend to be unstructured and thematically diverse. To address these challenges, this article presents a system prototype for harvesting, processing, modeling, and integrating heterogeneous social media feeds towards the generation of geosocial knowledge. Our article addresses primarily two key components of this system prototype: a novel data model for heterogeneous social media feeds and a corresponding general system architecture. We present these key components and demonstrate their implementation in our system prototype, GeoSocial Gauge.

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Section: Modeling Heterogeneous Geosocial Datamentioning

confidence: 99%

Geosocial gauge: a system prototype for knowledge discovery from social media

Croitoru

Crooks

Radzikowski

et al. 2013

International Journal of Geographical Information Science

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“…They differ from graphs by allowing an edge, denoted by a hyperedge, to connect with multiple nodes. Hypergraphs have been proposed in the context of recommendation generation, for the purpose of representing complex associations, such as social tagging (Jäschke et al, 2007;Berkovsky et al, 2007;Bu et al, 2010;Tan et al, 2011), where a tag is attached to an item by a user. If the tag, user, and item are represented by nodes, at least two edges are required to represent the association between the three entities 2 .…”

Section: Representing Social Data and Trust Using Graphsmentioning

confidence: 99%

Graph-based recommendations: from data representation to feature extraction and application

Tiroshi

Kuflik

Berkovsky

et al. 2019

Big Data Recommender Systems - Volume 2: Application Paradigms

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Modeling users for the purpose of identifying their preferences and then personalizing services on the basis of these models is a complex task, primarily due to the need to take into consideration various explicit and implicit signals, missing or uncertain information, contextual aspects, and more. In this study, a novel generic approach for uncovering latent preference patterns from user data is proposed and evaluated. The approach relies on representing the data using graphs, and then systematically extracting graph-based features and using them to enrich the original user models. The extracted features encapsulate complex relationships between users, items, and metadata. The enhanced user models can then serve as an input to any recommendation algorithm. The proposed approach is domain-independent (demonstrated on data from movies, music, and business recommender systems), and is evaluated using several state-of-the-art machine learning methods, on different recommendation tasks, and using different evaluation metrics. The results show a unanimous improvement in the recommendation accuracy across tasks and domains. In addition, the evaluation provides a deeper analysis regarding the performance of the approach in special scenarios, including high sparsity and variability of ratings.

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“…Recommender systems is a mature field where theoretical and practical innovations continue to command strong interest, speaking volumes of their utility. Recommender systems have been applied to movies [7,34], music [20,32], books [24], documents [25], e-learning [5], ecommerce [8], applications in markets [9] and web search [22]. Most widely used recommender systems have collaborative filtering (CF) [13,31] at their heart.…”

Section: Related Workmentioning

confidence: 99%

Clustered Monotone Transforms for Rating Factorization

Hiranandani

Somani

Koyejo

et al. 2019

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

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Exploiting low-rank structure of the user-item rating matrix has been the crux of many recommendation engines. However, existing recommendation engines force raters with heterogeneous behavior profiles to map their intrinsic rating scales to a common rating scale (e.g. 1-5). This non-linear transformation of the rating scale shatters the low-rank structure of the rating matrix, therefore resulting in a poor fit and consequentially, poor recommendations. In this paper, we propose Clustered Monotone Transforms for Rating Factorization (CMTRF), a novel approach to perform regression up to unknown monotonic transforms over unknown population segments. Essentially, for recommendation systems, the technique searches for monotonic transformations of the rating scales resulting in a better fit. This is combined with an underlying matrix factorization regression model that couples the user-wise ratings to exploit shared low dimensional structure. The rating scale transformations can be generated for each user, for a cluster of users, or for all the users at once, forming the basis of three simple and efficient algorithms proposed in this paper, all of which alternate between transformation of the rating scales and matrix factorization regression. Despite the non-convexity, CMTRF is theoretically shown to recover a unique solution under mild conditions. Experimental results on two synthetic and seven real-world datasets show that CMTRF outperforms other state-of-the-art baselines.

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Using rich social media information for music recommendation via hypergraph model

Cited by 50 publications

References 38 publications

Geosocial gauge: a system prototype for knowledge discovery from social media

Geosocial gauge: a system prototype for knowledge discovery from social media

Graph-based recommendations: from data representation to feature extraction and application

Clustered Monotone Transforms for Rating Factorization

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