Reciprocal recommendation systems are crucial for online dating platforms to provide quality matches and reduce choice overload. However, the design of reciprocal recommendation systems grapples with the challenges of estimating interpersonal compatibility and predicting the likelihood that two prospective partners will accept each other. Furthermore, despite the crucial role of users’ linguistic styles in determining user match decision-making, the contemporary design of such recommendation systems has not yet effectively incorporated this information. To bridge these gaps, we develop an end-to-end personalized Linguistic Style Matching-based Reciprocal Recommendation System (LS-RRS). We propose cross-user and within-user contrastive learning strategies combined with random masking to extract users’ linguistic styles and further integrate visual and textual information using an efficient convolution block. LS-RRS further models the matching probability using a conditional probability function and introduces a preference inflation factor on the receiver side to account for the asymmetric roles of the bilateral sides. The proposed model addresses the challenge of incorporating users’ linguistic styles into reciprocal recommendation and details the modeling of the two-stage matching process. Extensive experiments show that LS-RRS outperforms state-of-the-art models in recommendation performance, with a 29.35% increase in NDCG@10 when incorporating linguistic styles. Our follow-up analyses further validate the importance and effectiveness of the linguistic style extraction design through word-level and sentence-level visualizations, as well as qualitative case studies. This research contributes to the literature on reciprocal recommendation and offers a viable solution for alleviating user choice overload on online dating platforms.

Research on recommender systems has noted that the ranking of recommended items may play an important role in the performance of recommendation algorithms. To advance recommender systems research beyond the traditional approach that ranks recommended products in descending, it is crucial to understand the cognitive processes that online consumers experience when they evaluate products in a sequence. Drawing on evaluability theory and the order effects perspective, we formulate a scenario in which two products are presented sequentially and each product has two attributes, one of which can be evaluated independently while the other is difficult to evaluate without comparison. Analyses show that in two out of the three cases examined, presenting the most recommended product in the second place will result in stronger consumer purchase intentions and willingness to pay. Research hypotheses are proposed based on the results of the scenario analyses and are empirically tested through three laboratory experiments. In Study 1, evidence for the hypothesized order effects is found for the settings with randomly assigned product recommendations. In Study 2, the same effects are observed for the settings with personalized recommendations generated by a collaborative filtering algorithm. In Study 3, it is shown that such order effects also exist in terms of the recommendation strength of recommender systems. These findings provide novel insights into the behavioral implications of using recommender systems in e-commerce, shedding light on additional means of improving the design of such systems.

Voting mechanisms are widely adopted for evaluating the quality and credibility of user-generated content, such as online product reviews. For the reviews that do not receive sufficient votes, techniques and models are developed to automatically assess their helpfulness levels. Existing methods serving this purpose are mostly centered on feature analysis, ignoring the information conveyed in the frequencies and patterns of user votes. Consequently, the accuracy of helpfulness measurement is limited. Inspired by related findings from prediction theories and consumer behavior research, we propose a novel approach characterized by the technique of iterative Bayesian distribution estimation, aiming to more accurately measure the helpfulness levels of reviews used for training prediction models. Using synthetic data and a real-world data set involving 1.67 million reviews and 5.18 million votes from Amazon, a simulation experiment and a two-stage data experiment show that the proposed approach outperforms existing methods on accuracy measures. Moreover, an out-of-sample user study is conducted on Amazon Mechanical Turk. The results further illustrate the predictive power of the new approach. Practically, the research contributes to e-commerce by providing an enhanced method for exploiting the value of user-generated content. Academically, we contribute to the design science literature with a novel approach that may be adapted to a wide range of research topics, such as recommender systems and social media analytics.

Complements and substitutes are two typical product relationships that deserve consideration in online product recommendation. One of the key objectives of recommender systems is to promote cross-selling, which heavily relies on recommending the appropriate type of products in specific scenarios. Research on consumer behavior has shown that consumers usually prefer substitutes in the browsing stage whereas complements in the purchasing stage. Thus, it is of great importance to identify the complementary and substitutable relationships between products. In this article, we design a neural network based framework that integrates the textual content and non-textual information of online reviews to mine product relationships. For the textual content, we utilize methods such as LDA topic modeling to represent products in a succinct form called “embedding.” To capture the semantics of complementary and substitutable relationships, we design a modeling process that transfers the product embeddings into semantic features and incorporates additional non-textual factors of product reviews. Extensive experiments are conducted to verify the effectiveness of the proposed product relationship mining model. The advantages and robustness of our model are discussed from various perspectives.

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.

Accumulated literature on technology adoption research has suggested that cultural factors have important impacts on the cognition and behavior of information systems users. In this paper, the authors argue that cultural factors should be treated as aggregate characteristics at the population level instead of personal attributes at the individual level. The authors also propose that theoretical models could be developed for specific cultural contexts when examining IT/IS user behavior. In this regard, a model for analyzing user attitude toward mandatory use of information systems is proposed. Drawing on generally recognized cultural characteristics of China, three factors are introduced as determinants of user attitude—leader support, technology experience, and perceived fit. An empirical study is conducted with regard to the acceptance of a mobile municipal administration system in Beijing, China, for validating the proposed model with survey data and analyzing the adoption mechanism of the target system. The moderating roles of gender, age, and education level on the model are explored by interaction effect analyses and the findings provide helpful insights for related studies in other cultural contexts.

Based on information technology adoption theories and considering Chinese cultural characteristics, this article proposes a user centric IT/IS evaluation model composed of three determinants, namely perceived usefulness, perceived ease of use, and perceived fit, for investigating the e-government systems application and management in China. By empirically validating the model with survey data, it is demonstrated that the perceived fit has significant impacts on the end users’ evaluation towards e-government systems, due to the special element of Hexie in the Chinese culture. The results also indicate that the reasons for failures in e-government systems application in China largely lie in the lack of fit, which may root in the long power distance characteristic of the Far Eastern culture. The findings will provide scholars and practitioners with better understanding of the user evaluation regarding e-government systems in a Chinese cultural context.