Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders

In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named “Vision”. Within this recommender, selection criteria of candidate fields and contextual factors are designed and users’ dependencies on their personal pref-erence or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes

Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders

In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named “Vision”. Within this recommender, selection criteria of candidate fields and contextual factors are designed and users’ dependencies on their personal pref-erence or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes

SAR: A sentiment-aspect-region model for user preference analysis in geo-tagged reviews

Abstract—Many location based services, such as FourSquare, Yelp, TripAdvisor, Google Places, etc., allow users to compose reviews or tips on points of interest (POIs), each having a geographical coordinates. These services have accumulated a large amount of such geo-tagged review data, which allows deep analysis of user preferences in POIs. This paper studies two types of user preferences to POIs: topical-region preference and category aware topical-aspect preference. We propose a unified probabilistic model to capture these two preferences simultaneously. In addition, our model is capable of capturing the interaction of different factors, including topical aspect, sentiment, and spatial information. The model can be used in a number of applications, such as POI recommendation and user recommendation, among others. In addition, the model enables us to investigate whether people like an aspect of a POI or whether people like a topical aspect of some type of POIs (e.g., bars) in a region, which offer explanation for recommendations. Experiments on real world datasets show that the model achieves significant improvement in POI recommendation and user rec-ommendation in comparison to the state-of-the-art methods. We also propose an efficient online recommendation algorithm based on our model, which saves up to 90 % computation time. I

Modeling location-based user rating profiles for personalized recommendation

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

DYNAMIC ONTOLOGIES THAT ENCODE AND MANAGE RELEVANCE IN CONTEXT AWARE SYSTEMS

Context aware systems, to date, tend to fall into one of two categories: domain specific or generic across multiple domains. Domain specific systems are single-use instances – that is, establishing the ability to manage context for an additional domain necessitates the creation of an additional system. Authors of such systems should instead strive for generic ones. Generic context management systems require a generic modeling and context delivery system. Previous research has shown that generic context aware systems prove to be quite dynamic through their use of ontologies. These ontologies, however, are very rigid in nature, requiring additional software to mature and manage instantiated models, filter relevant information, or pre-cache information. The result is users who wish to use generic systems must encode relevance across ontological models, filters, and newly created external software with each re-use in order to manage context manipulation at run time. Through the design and implementation of Rover3, while leveraging the concept of an Automatic and Dynamic Information Model (ADIM) methodology, we outline what we believe how context aware systems should function. By providing a framework to encode relevance within ontologies, we minimize the way to present and consume relevant information. Our context management framework uses dynamic ontologies to deliver relevant information to users striving to achieve goals for any given situation. Walking through an accident response case study we showcase the aforementioned features of Rover3, showing how such incidents can benefit from context aware systems. The value of Rover3 is expressed through an extensibility study where efforts to expand existing ontological models are compared between Rover2 and Rover3. This dissertation presents: • The notion of relevant context and how it can be managed at runtime through a generic context aware system. • The required primitives and rules for modeling any generic situation. • The Automatic and Dynamic Information Model (ADIM) methodology, how one can encode relevance in a general information model, and exhaustive grammar and rules for this version of ADIM. • The Rover3 system and its application of ADIM, showcasing how it provides a generic framework to model and manage context that does not require any additional software