A location-based news article recommendation with explicit localized semantic analysis

The interest of users in handheld devices is strongly related to their location. Therefore, the user location is important, as a user context, for news article recommendation in a mobile environment. This paper proposes a novel news article recommendation that reflects the geographical context of the user. For this purpose, we propose the Explicit Localized Semantic Analysis (ELSA), an ESA-based topical representation of documents. Every location has its own geographical topics, which can be captured from the geo-tagged documents related to the location. Thus, not only news articles but locations are also represented as topic vectors. The main advantage of ELSA is that it stresses only the topics that are relevant to a given location, whereas all topics are equally important in ESA. As a result, geographical topics have different importance according to the user location in ELSA, even if they come from the same article. Another advantage of ELSA is that it allows a simple comparison of the user location and news articles, because it projects both locations and articles onto an identical space composed of Wikipedia topics. In the evaluation of ELSA with the New York Times corpus, it outperformed two simple baselines of Bag-Of-Words and LDA as well as two ESA-based methods. Rt10 of ELSA was improved up to 46.25% over other methods, and its email protected was always higher than those of the others regardless of k.