Soft-Constraint Based Online LDA for Community Recommendation

Chen

et al. 2015

ACM Trans. Inf. Syst.

143

Social media provides valuable resources to analyze user behaviors and capture user preferences. This article focuses on analyzing user behaviors in social media systems and designing a latent class statistical mixture model, named temporal context-aware mixture model (TCAM), to account for the intentions and preferences behind user behaviors. Based on the observation that the behaviors of a user in social media systems are generally influenced by intrinsic interest as well as the temporal context (e.g., the public's attention at that time), TCAM simultaneously models the topics related to users' intrinsic interests and the topics related to temporal context and then combines the influences from the two factors to model user behaviors in a unified way. Considering that users' interests are not always stable and may change over time, we extend TCAM to a dynamic temporal context-aware mixture model (DTCAM) to capture users' changing interests.To alleviate the problem of data sparsity, we exploit the social and temporal correlation information by integrating a social-temporal regularization framework into the DTCAM model. To further improve the performance of our proposed models (TCAM and DTCAM), an item-weighting scheme is proposed to enable them to favor items that better represent topics related to user interests and topics related to temporal context, respectively. Based on our proposed models, we design a temporal context-aware recommender system (TCARS). To speed up the process of producing the top-k recommendations from large-scale social media data, we develop an efficient query-processing technique to support TCARS. Extensive experiments have been conducted to evaluate the performance of our models on four real-world datasets crawled from different social media sites. The experimental results demonstrate the superiority of our models, compared with the state-of-the-art competitor methods, by modeling user behaviors more precisely and making more effective and efficient recommendations.

Section: Related Workmentioning

confidence: 92%

Dynamic User Modeling in Social Media Systems

Chen

et al. 2015

ACM Trans. Inf. Syst.

143

“…In Chen et al [2009], a hard-constraint-based LDA method was used to deal with user-community data, where each user is viewed as a document, and the communities that this user joins are viewed as words in the document. In contrast, Kang and Yu [2010] proposed a softconstraint-based LDA method for community recommendations. We refer to these topic model-based CF methods as "traditional" recommendation techniques which produce recommendations using non-spatial user activity history, failing to exploit the location information of user activities.…”

Section: Topic Modelmentioning

confidence: 92%

Lcars

Sun

et al. 2014

ACM Trans. Inf. Syst.

118

Newly emerging location-based and event-based social network services provide us with a new platform to understand users' preferences based on their activity history. A user can only visit a limited number of venues/events and most of them are within a limited distance range, so the user-item matrix is very sparse, which creates a big challenge to the traditional collaborative filtering-based recommender systems. The problem becomes even more challenging when people travel to a new city where they have no activity information.In this article, we propose LCARS, a location-content-aware recommender system that offers a particular user a set of venues (e.g., restaurants and shopping malls) or events (e.g., concerts and exhibitions) by giving consideration to both personal interest and local preference. This recommender system can facilitate people's travel not only near the area in which they live, but also in a city that is new to them. Specifically, LCARS consists of two components: offline modeling and online recommendation. The offline modeling part, called LCA-LDA, is designed to learn the interest of each individual user and the local preference of each individual city by capturing item cooccurrence patterns and exploiting item contents. The online recommendation part takes a querying user along with a querying city as input, and automatically combines the learned interest of the querying user and the local preference of the querying city to produce the topk recommendations. To speed up the online process, a scalable query processing technique is developed by extending both the Threshold Algorithm (TA) and TA-approximation algorithm. We evaluate the performance of our recommender system on two real datasets, that is, DoubanEvent and Foursquare, and one large-scale synthetic dataset. The results show the superiority of LCARS in recommending spatial items for users, especially when traveling to new cities, in terms of both effectiveness and efficiency. Besides, the experimental analysis results also demonstrate the excellent interpretability of LCARS.

“…In Chen et al [2009], a hard-constraint-based LDA method was used to deal with user community data, where each user is viewed as a document and the communities that this user joins are viewed as words in the document. In contrast, Kang and Yu [2010] proposed a soft-constraint-based LDA method for community recommendations. We refer to these topic model-based CF methods as "traditional" recommendation techniques that produce recommendations using nonspatial user ratings for nonspatial items represented as the triple (user, score, item), failing to exploit the location information of users and/or items.…”

Section: Topic Modelmentioning

confidence: 92%

“…-LDA. Following the previous works of [Jin et al 2005;Chen et al 2009;Kang and Yu 2010], an LDA-based method is implemented to deal with user rating data and model user preferences, where each user is viewed as a document, and the items rated by the user are viewed as the words appearing in the document. It should be noted that this baseline method does not consider/exploit either user or item location information.…”

Section: Comparison Approachesmentioning

confidence: 99%

Modeling Location-Based User Rating Profiles for Personalized Recommendation

ACM Trans. Knowl. Discov. Data

Chen

et al. 2015

110

This article proposes LA-LDA, a location-aware probabilistic generative model that exploits location-based ratings to model user profiles and produce recommendations. Most of the existing recommendation models do not consider the spatial information of users or items; however, LA-LDA supports three classes of locationbased ratings, namely spatial user ratings for nonspatial items, nonspatial user ratings for spatial items, and spatial user ratings for spatial items. LA-LDA consists of two components, ULA-LDA and ILA-LDA, which are designed to take into account user and item location information, respectively. The component ULA-LDA explicitly incorporates and quantifies the influence from local public preferences to produce recommendations by considering user home locations, whereas the component ILA-LDA recommends items that are closer in both taste and travel distance to the querying users by capturing item co-occurrence patterns, as well as item location co-occurrence patterns. The two components of LA-LDA can be applied either separately or collectively, depending on the available types of location-based ratings. To demonstrate the applicability and flexibility of the LA-LDA model, we deploy it to both top-k recommendation and cold start recommendation scenarios. Experimental evidence on large-scale real-world data, including the data from Gowalla (a location-based social network), DoubanEvent (an event-based social network), and MovieLens (a movie recommendation system), reveal that LA-LDA models user profiles more accurately by outperforming existing recommendation models for top-k recommendation and the cold start problem. . 2015. Modeling location-based user rating profiles for personalized recommendation.