Location recommendation is an important means to help people discover attractive locations. However, extreme sparsity of user-location matrices leads to a severe challenge, so it is necessary to take implicit feedback characteristics of user mobility data into account and leverage location's spatial information. To this end, based on previously developed GeoMF, we propose a scalable and flexible framework, dubbed GeoMF++, for joint geographical modeling and implicit feedback based matrix factorization. We then develop an efficient optimization algorithm for parameter learning, which scales linearly with data size and the total number of neighbor grids of all locations. GeoMF++ can be well explained from two perspectives. First, it subsumes two-dimensional kernel density estimation so that it captures spatial clustering phenomenon in user mobility data; Second, it is strongly connected with widely-used neighbor additive models and graph Laplacian regularized models. We finally evaluate GeoMF++ on two large-scale LBSN datasets with respect to both warm-start and cold-start scenarios. The experimental results show that GeoMF++ consistently outperforms the state-of-the-arts and other competing baselines on both datasets in terms of NDCG and Recall. Besides, the efficiency studies show that GeoMF++ is much more scalable with the increase of data size and the dimension of latent space.
When content consumers explicitly judge content positively, we consider them to be engaged. Unfortunately, explicit user evaluations are difficult to collect, as they require user effort. Therefore, we propose to use device interactions as implicit feedback to detect engagement. We assess the usefulness of swipe interactions on tablets for predicting engagement, and make the comparison with using traditional features based on time spent. We gathered two unique datasets of more than 250,000 swipes, 100,000 unique article visits, and over 35,000 explicitly judged news articles, by modifying two commonly used tablet apps of two newspapers. We tracked all device interactions of 407 experiment participants during one month of habitual news reading. We employed a behavioral metric as a proxy for engagement, because our analysis needed to be scalable to many users, and scanning behavior required us to allow users to indicate engagement quickly. We point out the importance of taking into account content ordering, report the most predictive features, zoom in on briefly read content and on the most frequently read articles. Our findings demonstrate that fine-grained tablet interactions are useful indicators of engagement for newsreaders on tablets. The best features successfully combine both time-based aspects and swipe interactions.
We propose the Neural Vector Space Model (NVSM), a method that learns representations of documents in an unsupervised manner for news article retrieval. In the NVSM paradigm, we learn low-dimensional representations of words and documents from scratch using gradient descent and rank documents according to their similarity with query representations that are composed from word representations. We show that NVSM performs better at document ranking than existing latent semantic vector space methods. The addition of NVSM to a mixture of lexical language models and a state-of-the-art baseline vector space model yields a statistically significant increase in retrieval effectiveness. Consequently, NVSM adds a complementary relevance signal. Next to semantic matching, we find that NVSM performs well in cases where lexical matching is needed. NVSM learns a notion of term specificity directly from the document collection without feature engineering. We also show that NVSM learns regularities related to Luhn significance. Finally, we give advice on how to deploy NVSM in situations where model selection (e.g., cross-validation) is infeasible. We find that an unsupervised ensemble of multiple models trained with different hyperparameter values performs better than a single cross-validated model. Therefore, NVSM can safely be used for ranking documents without supervised relevance judgments.
Quote is a language phenomenon of transcribing the saying of someone else, like a proverb or a famous saying. Proper usage of quote can usually make the statement more elegant and convincing. However, sometimes we may be so eager to use a quote somewhere, but have no idea about the relevant quotes to express our idea. Therefore it is exciting to have a recommendation system to recommend potential quotes for use while writing. This paper proposes a novel task of quote recommendation, and present a quote recommendation system called QuoteRec. Two models are proposed to learn the vector representations of quotes and contexts, and then rank the candidate quotes according to the representations. The first model learns the quote representation according to the contexts of a quote. The second model is a neural network model, which learns the representation of a quote by concerning both its content and contexts. Moreover, in the second model a mechanism is introduced to model the subtle usage of word meaning in the quote. Experimental results show that the proposed models are quite effective at learning the semantic representations of quotes, and the neural network model achieves state-of-the-art results on the quote recommendation task.
This paper presents a framework for speedy video matching and retrieval through detection and measurement of visual similarity. The frameworks efficiency stems from its power to encode a given shot content into compact fixed length signature that facilitates towards a robust real-time matching. Separate scene and motion signatures are developed and fused together to fully represent and match respective video shots. The framework works on thumbnail images (DC-image from the MPEG stream). Scene information is captured through the Statistical Dominant Colour Profile (SDCP), while motion information is captured through a graph-based signature, called the Dominant Colour Graph Profile (DCGP). The SDCP is a fixed-length compact signature that statistically encodes the colours spatio-temporal patterns across video frames. The DCGP is a fixed-length signature that records and tracks blocks movement across video frames, where the graph structural properties are used to extract the signature values. Finally, the overall video signature is generated by fusing the individual scene and motion signatures, where the matching is done by directly comparing the respective fused video signatures. The signature-based aspect of the proposed framework is the key to its high matching speed (>2000 fps), compared to the current techniques that relies on exhaustive processing, such as dense sampling.