More often than not, spatial objects are associated with some context, in the form of text, descriptive tags (e.g., points of interest, flickr photos), or linked entities in semantic graphs (e.g., Yago2, DBpedia). Hence, location-based retrieval should be extended to consider not only the locations but also the context of the objects, especially when the retrieved objects are too many and the query result is overwhelming. In this article, we study the problem of selecting a subset of the query result, which is the most representative. We argue that objects with similar context and nearby locations should proportionally be represented in the selection. Proportionality dictates the pairwise comparison of all retrieved objects and hence bears a high cost. We propose novel algorithms which greatly reduce the cost of proportional object selection in practice. In addition, we propose pre-processing, pruning, and approximate computation techniques that their combination reduces the computational cost of the algorithms even further. We theoretically analyze the approximation quality of our approaches. Extensive empirical studies on real datasets show that our algorithms are effective and efficient. A user evaluation verifies that proportional selection is more preferable than random selection and selection based on object diversification.

More often than not, spatial objects are associated with some context, in the form of text, descriptive tags (e.g. points of interest, flickr photos), or linked entities in semantic graphs (e.g. Yago2, DBpedia). Hence, location-based retrieval should be extended to consider not only the locations but also the context of the objects, especially when the retrieved objects are too many and the query result is overwhelming. In this paper, we study the problem of selecting a subset of the query result, which is the most representative. We argue that objects with similar context and nearby locations should proportionally be represented in the selection. Proportionality dictates the pairwise comparison of all retrieved objects and hence bears a high cost. We propose novel algorithms which greatly reduce the cost of proportional object selection in practice. Extensive empirical studies on real datasets show that our algorithms are effective and efficient. A user evaluation verifies that proportional selection is more preferable than random selection and selection based on object diversification.

Abstract The abundance and ubiquity of RDF data (such as DBpedia and YAGO2) necessitate their effective and efficient retrieval. For this purpose, keyword search paradigms liberate users from understanding the RDF schema and the SPARQL query language. Popular RDF knowledge bases (e.g., YAGO2) also include spatial semantics that enable location-based search. In an earlier location-based keyword search paradigm, the user inputs a set of keywords, a query location, and a number of RDF spatial entities to be retrieved. The output entities should be geographically close to the query location and relevant to the query keywords. However, the results can be similar to each other, compromising query effectiveness. In view of this limitation, we integrate textual and spatial diversification into RDF spatial keyword search, facilitating the retrieval of entities with diverse characteristics and directions with respect to the query location. Since finding the optimal set of query results is NP-hard, we propose two approximate algorithms with guaranteed quality. Extensive empirical studies on two real datasets show that the algorithms only add insignificant overhead compared to non-diversified search, while returning results of high quality in practice (which is verified by a user evaluation study we conducted).

Recommending venues or points-of-interest (POIs) is a hot topic in recent years, especially for tourism applications and mobile users. We propose and evaluate several suggestion methods, taking an effectiveness, feasibility, efficiency, and privacy perspective. The task is addressed by two content-based methods (a Weighted kNN classifier and a Rated Rocchio personalized query), Collaborative Filtering methods, as well as several (rank-based or rating-based) methods of merging results of different systems. Effectiveness is evaluated on two standard benchmark datasets, provided and used by TREC’s Contextual Suggestion Tracks in 2015 and 2016. First, we enrich these datasets with more information on venues, collected from web services like Foursquare and Yelp; we make this extra data available for future experimentation. Then, we find that the content-based methods provide state-of-the-art effectiveness, the collaborative filtering variants mostly suffer from data sparsity problems in the current datasets, and the merging methods further improve results by mainly promoting the first relevant suggestion. Concerning mobile feasibility, efficiency, and user privacy, the content-based methods, especially Rated Rocchio, are the best. Collaborative filtering has the worst efficiency and privacy leaks. Our findings can be very useful for developing effective and efficient operational systems, respecting user privacy. Last, our experiments indicate that better benchmark datasets would be welcome, and the use of additional evaluation measures—more sensitive in recall—is recommended.