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1.
People are gregarious by nature, which explains why group activities, from colleagues sharing a meal to friends attending a book club event together, are the social norm. Online group recommenders identify items of interest, such as restaurants, movies, and books, that satisfy the collective needs of a group (rather than the interests of individual group members). With a number of new movies being released every week, online recommenders play a significant role in suggesting movies for family members or groups of friends/people to watch, either at home or at movie theaters. Making group recommendations relevant to the joint interests of a group, however, is not a trivial task due to the diversity in preferences among group members. To address this issue, we introduce GroupReM which makes movie recommendations appealing (to a certain degree) to members of a group by (i) employing a merging strategy to explore individual group members’ interests in movies and create a profile that reflects the preferences of the group on movies, (ii) using word-correlation factors to find movies similar in content, and (iii) considering the popularity of movies at a movie website. Unlike existing group recommenders based on collaborative filtering (CF) which consider ratings of movies to perform the recommendation task, GroupReM primarily employs (personal) tags for capturing the contents of movies considered for recommendation and group members’ interests. The design of GroupReM, which is simple and domain-independent, can easily be extended to make group recommendations on items other than movies. Empirical studies conducted using more than 3000 groups of different users in the MovieLens dataset, which are various in terms of numbers and preferences in movies, show that GroupReM is highly effective and efficient in recommending movies appealing to a group. Experimental results also verify that GroupReM outperforms popular CF-based recommenders in making group recommendations. 相似文献
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As exhibitions are known to play important roles in marketing and sales promotion, the exhibition industry has grown significantly not only in the exhibition event size and frequency but also in the number of participating firms and visitors. While the challenge in assessing economic returns from exhibitions is being studied, it is agreed that the eventual success of an exhibition resides largely in its ability to meet the visitors’ needs. Visitors use an exhibition as a source of information when searching for products or services. Though an exhibition provides an information-rich environment, however, visitors often get lost in the abundance of information. A specialized recommender system can be a good solution to information overload as it can guide visitors to right exhibition booths and help them collect necessary information. Traditional collaborative-filtering recommender systems, however, use only customers’ rating or purchase records so that they do not capture exhibition visitors’ temporal visit sequences and dynamic preferences. Moreover, due to the computation overhead, they cannot generate real-time recommendation in ubiquitous environments for exhibitions. In order to overcome these drawbacks, this study proposes a booth recommendation procedure that takes into consideration not only booth visit records but also visit sequences. Experiment results show that the proposed procedure achieves higher recommendation accuracy, faster computation, and more diversity than a typical collaborative-filtering recommender system. From the results, we conclude that the proposed booth recommendation procedure is suitable for real-time recommendation in ubiquitous exhibition environments. 相似文献
3.
《Information processing & management》2016,52(3):459-477
Nowadays, the increasing demand for group recommendations can be observed. In this paper we address the problem of recommendation performance for groups of users (group recommendation). We focus on the performance of very Top-N recommendations, which are important when recommending the long lasting items (only a few such items are consumed per session, e.g. movie). To improve existing group recommenders we propose a mixed hybrid recommender for groups combining content-based and collaborative strategies. The principle of proposed group recommender is to generate content and collaborative recommendations for each user, apply an aggregation strategy to solve the group conflict preferences for the content and collaborative sets separately, and finally reorder the collaborative candidates based on the content-based ones. It is based on an idea that candidates recommended by both recommendation strategies at the same time are presumably more appropriate for the group than the candidates recommended by individual strategies. The evaluation is performed by several experiments in the multimedia domain (as typical representative for group recommendations). Both, online and offline experiments were performed in order to compare real users’ satisfaction to the standard group recommenders and also, to compare performance of proposed approach to the state-of-the-art recommenders based on the MovieLens dataset. Finally, we experimented with the proposed hybrid recommender to generate the recommendation for a group of size one (i.e. single user recommendation). Obtained results, support our hypothesis that proposed mixed hybrid approach improves the precision of the recommendation for groups of users and for the single-user recommendation respectively on very Top-N recommended items. 相似文献
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《International Journal of Information Management》2016,36(3):397-402
Online video recommender systems help users find videos suitable for their preferences. However, they have difficulty in identifying dynamic user preferences. In this study, we propose a new recommendation procedure using changes of users’ facial expressions captured every moment. Facial expressions portray the users’ actual emotions about videos. We can utilize them to discover dynamic user preferences. Further, because the proposed procedure does not rely on historical rating or purchase records, it properly addresses the new user problem, that is, the difficulty in recommending products to users whose past rating or purchase records are not available. To validate the recommendation procedure, we conducted experiments with footwear commercial videos. Experiment results show that the proposed procedure outperforms benchmark systems including a random recommendation, an average rating approach, and a typical collaborative filtering approach for recommendation to both new and existing users. From the results, we conclude that facial expressions are a viable element in recommendation. 相似文献
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《Information processing & management》2023,60(5):103464
Existing approaches to learning path recommendation for online learning communities mainly rely on the individual characteristics of users or the historical records of their learning processes, but pay less attention to the semantics of users’ postings and the context. To facilitate the knowledge understanding and personalized learning of users in online learning communities, it is necessary to conduct a fine-grained analysis of user data to capture their dynamical learning characteristics and potential knowledge levels, so as to recommend appropriate learning paths. In this paper, we propose a fine-grained and multi-context-aware learning path recommendation model for online learning communities based on a knowledge graph. First, we design a multidimensional knowledge graph to solve the problem of monotonous and incomplete entity information presentation of the single layer knowledge graph. Second, we use the topic preference features of users’ postings to determine the starting point of learning paths. We then strengthen the distant relationship of knowledge in the global context using the multidimensional knowledge graph when generating and recommending learning paths. Finally, we build a user background similarity matrix to establish user connections in the local context to recommend users with similar knowledge levels and learning preferences and synchronize their subsequent postings. Experiment results show that the proposed model can recommend appropriate learning paths for users, and the recommended similar users and postings are effective. 相似文献
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[目的/意义] 近些年来,在线健康社区变得越来越流行。然而,较少社区能成功地维持用户并激励他们持续的分享知识。本文将社会支持理论和承诺-信任理论结合起来,构建一个集成模型来研究在线健康社区用户的持续知识分享意愿的影响因素。[方法/过程] 根据获得的475份有效调查问卷,本文使用SPSS20.0和AMOS20.0检验所提出的假设。[结果/结论] 研究发现,信息支持、情感支持对满意度和信任均有显著影响,网络支持仅对满意度有显著影响。此外,满意度和信任除了对持续知识分享意愿有直接正向影响外,还通过关系承诺的中介,有间接的作用。研究有助于深化对知识分享行为的认识以及帮助在线健康社区的管理者更好地维系用户。 相似文献
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Users of Social Networking Sites (SNSs) like Facebook, LinkedIn or Twitter, are facing two problems: (1) it is difficult for them to keep track of their social friendships and friends’ social activities scattered across different SNSs; and (2) they are often overwhelmed by the huge amount of social data (friends’ updates and other activities). To address these two problems, we propose a user-centric system called “SocConnect” (Social Connect) for aggregating social data from different SNSs and allowing users to create personalized social and semantic contexts for their social data. Users can blend and group friends on different SNSs, and rate the friends and their activities as favourite, neutral or disliked. SocConnect then provides personalized recommendation of friends’ activities that may be interesting to each user, using machine learning techniques. A prototype is also implemented to demonstrate these functionalities of SocConnect. Evaluation on real users confirms that users generally like the proposed functionalities of our system, and machine learning can be effectively applied to provide personalized recommendation of friends’ activities and help users deal with cognitive overload. 相似文献
10.
Jaeki Song Jeff Baker Sangno Lee James C. Wetherbe 《International Journal of Information Management》2012
Retailers’ websites are an important interface between retailers and their customers. The various features and elements that retailers include on their websites play a critical role in attracting customers and ensuring their satisfaction with the online shopping process. By conducting a three-phased study, we identify how the elements of a website shape customers’ salient beliefs. These salient beliefs, in turn, determine the level of customers’ satisfaction with the website. This study provides both theoretical insights into the beliefs of online shoppers and practical insights for retail website operators. Specifically, we argue that when retail websites are constructed to include several specific elements that appeal to the key salient beliefs of information quality, service quality, and system quality, retailers will increase customers’ satisfaction with the online shopping process. 相似文献
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《Information processing & management》2023,60(4):103402
In community-based social media, users consume content from multiple communities and provide feedback. The community-related data reflect user interests, but they are poorly used as additional information to enrich user-content interaction for content recommendation in existing studies. This paper employs an information seeking behavior perspective to describe user content consumption behavior in community-based social media, therefore revealing the relations between user, community and content. Based on that, the paper proposes a Community-aware Information Seeking based Content Recommender (abbreviated as CISCRec) to use the relations for better modeling user preferences on content and increase the reasoning on the recommendation results. CISCRec includes two key components: a two-level TransE prediction framework and interaction-aware embedding enhancement. The two-level TransE prediction framework hierarchically models users’ preferences for content by considering community entities based on the TransE method. Interaction-aware embedding enhancement is designed based on the analysis of users’ continued engagement in online communities, aiming to add expressiveness to embeddings in the prediction framework. To verify the effectiveness of the model, the real-world Reddit dataset (4,868 users, 115,491 contents, 850 communities, and 602,025 interactions) is chosen for evaluation. The results show that CISCRec outperforms 8 common baselines by 9.33%, 4.71%, 42.13%, and 14.36% on average under the Precision, Recall, MRR, and NDCG respectively. 相似文献
12.
The micro-determinants of meso-level learning and innovation: evidence from a Chilean wine cluster 总被引：7，自引：0，他引：7
Most analyses of the relationship between spatial clustering and the technological learning of firms have emphasised the influence of the former on the latter, and have focused on intra-cluster learning as the driver of innovative performance. This paper reverses those perspectives. It examines the influence of individual firms’ absorptive capacities on both the functioning of the intra-cluster knowledge system and its interconnection with extra-cluster knowledge. It applies social network analysis to identify different cognitive roles played by cluster firms and the overall structure of the knowledge system of a wine cluster in Chile. The results show that knowledge is not diffused evenly ‘in the air’, but flows within a core group of firms characterised by advanced absorptive capacities. Firms’ different cognitive roles include some—as in the case of technological gatekeepers—that contribute actively to the acquisition, creation and diffusion of knowledge. Others remain cognitively isolated from the cluster, though in some cases strongly linked to extra-cluster knowledge. Possible implications for policy are noted. 相似文献
13.
In collaborative filtering recommender systems recommendations can be made to groups of users. There are four basic stages in the collaborative filtering algorithms where the group’s users’ data can be aggregated to the data of the group of users: similarity metric, establishing the neighborhood, prediction phase, determination of recommended items. In this paper we perform aggregation experiments in each of the four stages and two fundamental conclusions are reached: (1) the system accuracy does not vary significantly according to the stage where the aggregation is performed, (2) the system performance improves notably when the aggregation is performed in an earlier stage of the collaborative filtering process. This paper provides a group recommendation similarity metric and demonstrates the convenience of tackling the aggregation of the group’s users in the actual similarity metric of the collaborative filtering process. 相似文献
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Though much information behaviour takes place in collaborative settings, information behaviour processes are commonly perceived and modelled by information scientists as individual processes. The paper presents and discusses the findings from a qualitative preliminary case study exploring Kuhlthau’s Information Search Process (ISP) model in a group-based educational setting. The aim of the study was to explore if members of a group behave differently from the individual modelled in the ISP model and further, if members of a group demonstrate different behaviours or they will assimilate and turn the group into ‘an individual’, just in another sense. During a project assignment, which lasted seven weeks, two groups of information science students filled out a questionnaire and kept diaries of their activities and information-related behaviour. Further, the students were interviewed three times each during the study. 相似文献
15.
This paper reports on the findings from a longitudinal case study exploring Kuhlthau’s information search process (ISP)-model in a group based academic setting. The research focus is on group members’ activities and cognitive and emotional experiences during the task process of writing an assignment. It is investigated if group members’ information behavior differ from the individual information seeker in the ISP-model and to what extent this behavior is influenced by contextual (work task) and social (group work) factors. Three groups of LIS students were followed during a 14 weeks period in 2004/2005 (10 participants). Quantitative and qualitative methods were employed, such as demographic surveys, process surveys, diaries and interviews. Similarities in behavior were found between group members and the individual in Kuhlthau’s ISP-model with regard to the general stages of information seeking, the cognitive pattern associated with focus formulation and the tendency towards an increase in writing activities while searching activities decreased. Differences in behavior were also found, which were associated with contextual and social factors beyond the mere search process. It is concluded that the ISP-model does not fully comply with group members’ problem solving process and the involved information seeking behavior. Further, complex academic problem solving seems to be even more complex when it is performed in a group based setting. The study contributes with a new conceptual understanding of students’ behavior in small groups. 相似文献
16.
《International Journal of Information Management》2014,34(2):311-314
Twenty-first century's advancement in information technologies and the emergence of online communities have considerably influenced the online communication channels between patients and health service providers. Online health communities are now popular venues for health information sharing, yet little is known about the benefits in developing countries such as Iran. The aim of this case is to investigate on online health communities in Iran and to have a better understanding of consumer's behaviour using health services. The case integrates social support theory and social media concepts with traditional consumer behaviour theory, notably satisfaction. Using a content analysis of three online health communities indicates the value of social media in developing service quality in health industry. 相似文献
17.
This study examines the effects of the differences in organizational identities that emerged during a post-merger project that aimed at unifying the laboratory services of a large healthcare center that resulted from the merging of three hospitals by supporting them with a unique information system. We draw on the concepts of organizational identity and sensemaking to analyze the laboratory information system implementation project. Organizational identity is conceptualized as the mental representation that organizational members have of themselves as a social group in terms of practices, norms, and values and how they understand themselves to be different from members of other organizations. Data analysis suggests that divergent organizational identities and team members’ alternative interpretations of others’ practices, norms and organizational symbols, coexist during the post-merger integration phase. These interpretations are reflected in the final functionality of the information system that was different from the planned one. 相似文献
18.
David Zeitlyn 《Research Policy》2003,32(7):1287-1291
Building on Eric Raymond’s work this article discusses the motivation and rewards that lead some software engineers to participate in the open source movement. It is suggested that software engineers in the open source movement may have sub-groupings which parallel kinship groups such as lineages. Within such groups gift giving is not necessarily or directly reciprocated, instead members work according to the ‘axiom of kinship amity’—direct economic calculation is not appropriate within the group. What Bourdieu calls ‘symbolic capital’ can be used to understand how people work in order to enhance the reputation (of themselves and their group). 相似文献
19.
Xiao-Ling Jin Zhongyun Zhou Matthew K.O. Lee Christy M.K. Cheung 《International Journal of Information Management》2013
This study theorized and validated a model of knowledge sharing continuance in a special type of online community, the online question answering (Q&A) community, in which knowledge exchange is reflected mainly by asking and answering specific questions. We created a model that integrated knowledge sharing factors and knowledge self-efficacy into the expectation confirmation theory. The hypotheses derived from this model were empirically validated using an online survey conducted among users of a famous online Q&A community in China, “Yahoo! Answers China”. The results suggested that users’ intention to continue sharing knowledge (i.e., answering questions) was directly influenced by users’ ex-post feelings as consisting of two dimensions: satisfaction, and knowledge self-efficacy. Based on the obtained results, we also found that knowledge self-efficacy and confirmation mediated the relationship between benefits and satisfaction. 相似文献
20.
In online travel communities, ‘Top-K best places to visit’ recommendations are gaining more attention from travelers due to their ubiquitous access to the Internet, but little empirical effort has been made to investigate what factors lead to the popularity of user-curated ‘best places to visit (BP2V)’ recommendations. This research therefore aims to identify and validate the heuristic factors affecting the popularity of BP2V recommendations. Based on the heuristic-systematic model (HSM) of persuasion, we derive recommender-related (i.e., recommender's identity disclosure, reputation, experience, and location of residency) and recommendation-related (i.e., number of places recommended, helpfulness rating, number of comments added, and length of recommendation) heuristic characteristics of BP2V recommendations and investigate their impact on recommendation popularity. In addition, this study examines the moderating effect of destination category (i.e., attractions, food, shopping, and activities) on the relationship between heuristic characteristics and the popularity of BP2V recommendations. Our empirical results, which were based on 565 ‘best places to visit in the U.S.’ recommendation postings from Qyer.com, a major online travel community in China, suggest that recommender's identity disclosure, reputation, number of places recommended, helpfulness rating, and length of recommendation are positively associated with recommendation popularity. We also found that the relationships between heuristic factors and the popularity of BP2V recommendations are contingent on destination category. This study will contribute to the body of knowledge on online travel communities and HSM and provide valuable implications for general travelers and managers in the tourism and hospitality industry. 相似文献