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 LPESA. 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, LPDSA offers more effective (19:8% to 179:6% better) online news recommendation with much lower time cost (25 times quicker) than LP-ESA.
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