A valuable small subset strategically selected from massive online reviews is beneficial to improve consumers’ decision-making efficiency in e-commerce. Existing review selection methods primarily concentrate on the informativeness of reviews and aim to find a subset of reviews that can reflect the informational properties of the original review set. However, changes in consumers’ review diets during the two-phase decision process are not fully considered. In this study, we propose a novel review selection problem of finding a diet-matched review subset with high diversity and representativeness, which can better adapt to consumers’ review-diet conversion from attribute-oriented to experience-oriented reviews between two decision phases. A novel decision-phase-based review selection method named DPRS is further proposed, which involves two steps: review classification and review selection. In the review classification step, the probability of a review being attribute-oriented or experience-oriented is estimated by prior knowledge-aware attentive neural network. In the second step, a novel heuristic algorithm, namely, stepwise non-dominated selection with superiority strategy, is introduced to seek the solution to the review selection problem. Extensive experiments on a real-world dataset demonstrate that DPRS outperforms state-of-the-art methods in terms of both review classification and review selection.

Concerning the information overload of online reviews, this paper models a new review selection problem called the Informative Review Subset Selection problem (namely, IRSS) and demonstrates that it is NP-hard to solve and approximate. Furthermore, a novel heuristic method (namely, Combined Search-ComS) is proposed for seeking the solution to the problem and selecting a subset of reviews, which is consistent with the original review corpus in light of mutual information entropy. The proposed method is then comprehensively examined via extensive data experiments and a user study on Amazon data. Experimental results reveal the overall superiority of the proposed method in comparison with other extant methods of concern, showing that it is an effective way to select an informative subset of online reviews. The proposed method is deemed desirable and useful for online consumers and service providers.

Recent years have witnessed a rapid increase in online data volume and the growing challenge of information overload for web use and applications. Thus, information diversity is of great importance to both information service providers and users of search services. Based on a diversity evaluation measure (namely, information coverage), a heuristic method—FastCovC+S-Select—with corresponding algorithms is designed on the greedy submodular idea. First, we devise the CovC+S-Select algorithm, which possesses the characteristic of asymptotic optimality, to optimize information coverage using a strategy in the spirit of simulated annealing. To accelerate the efficiency of CovC+S-Select, its fast approximation (i.e., FastCovC+S-Select) is then developed through a heuristic strategy to downsize the solution space with the properties of information coverage. Furthermore, ample experiments have been conducted to show the effectiveness, efficiency, and parameter robustness of the proposed method, along with comparative analyses revealing the performance’s advantages over other related methods. The online appendix is available at https://doi.org/10.1287/ijoc.2017.0753 .

The consistency between review summaries and review ranking lists is important for consumers so they can utilize online reviews effectively and efficiently in their purchase decisions. This paper examines this consistency issue and formulates it as an optimization problem. Based on consumers’ reading behaviors, all possible sets of reviews that consumers would read from ranking lists are considered; the objective is to maximize the expected consistency. Because of the NP-hardness of the problem, exact methods that search for the optimal ranking lists are generally not acceptable in practice. Hence, a heuristic approach (the enhanced stepwise optimization procedure) is proposed. This approach is an effective and efficient approximation that selects reviews iteratively to add to the ranking lists in light of expected consistency value, superiority, and execution time. Intensive experiments on both synthetic and real data are conducted, with various environments and settings, along with a relevant user study, revealing that the proposed approach outperforms other related methods.

Competitiveness degree analysis is a focal point of business strategy and competitive intelligence, aimed to help managers closely monitor to what extent their rivals are competing with them. This article proposes a novel method, namely BCQ, to measure the competitiveness degree between peers from query logs as an important form of user generated contents, which reflects the “wisdom of crowds” from the search engine users’ perspective. In doing so, a bipartite graph model is developed to capture the competitive relationships through conjoint attributes hidden in query logs, where the notion of competitiveness degree for entity pairs is introduced, and then used to identify the competitive paths mapped in the bipartite graph. Subsequently, extensive experiments are conducted to demonstrate the effectiveness of BCQ to quantify the competitiveness degrees. Experimental results reveal that BCQ can well support competitors ranking, which is helpful for devising competitive strategies and pursuing market performance. In addition, efficiency experiments on synthetic data show a good scalability of BCQ on large scale of query logs.

Extracting representative information is of great interest in data queries and web applications nowadays, where approximate match between attribute values/records is an important issue in the extraction process. This paper proposes an approach to extracting representative tuples from data classes under an extended possibility-based data model, and to introducing a measure (namely, relation compactness) based upon information entropy to reflect the degree that a relation is compact in light of information redundancy. Theoretical analysis and data experiments show that the approach has desirable properties that: 1) the set of representative tuples has high degrees of compactness (less redundancy) and coverage (rich content); 2) it provides a way to obtain data query outcomes of different sizes in a flexible manner according to user preference; and 3) the approach is also meaningful and applicable to web search applications.