Location−Aware Personalized News Recommendation with Deep Semantic Analysis
Cheng Chen‚ Xiangwu Meng‚ Zhenghua Xu and Thomas Lukasiewicz.
With the popularity of mobile devices and the quick growth of the mobile Web, users can now browse news wherever they want; so, their news preferences are usually related to their geographical contexts. Consequently, many research efforts have been put on location-aware news recommendation, which recommends to users news happening nearest to them. Nevertheless, in a real-world context, users’ news preferences are not only related to their locations, but also strongly related to their personal interests. Therefore, in this paper, we propose a hybrid method called location-aware personalized news recommendation with explicit semantic analysis (LP-ESA), which recommends news using both the users’ personal interests and their geographical contexts. However, the Wikipedia-based topic space in LP-ESA suffers from the problems of high dimensionality, sparsity, and redundancy, which greatly degrade the performance of LP-ESA. To address these problems, we further propose a novel method called LP-DSA to exploit recommendation-oriented deep neural networks to extract dense, abstract, low dimensional, and effective feature representations for users, news, and locations. Experimental results show that LP-ESA and LP-DSA both significantly outperform the state-of-the-art baselines. In addition, LP-DSA offers more effective (19.8%–179.6% better) online news recommendation with much lower time cost (25 times quicker) than LP-ESA.