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《Information processing & management》2023,60(5):103416
Recently, graph neural network (GNN) has been widely used in sequential recommendation because of its powerful ability to capture high-order collaborative relations, greatly promoting recommendation performance. However, some existing GNN-based methods fail to make full use of multiple relevant features of nodes and ignore the impact of semantic association between nodes on extracting user preferences. To this end, we propose a multi-feature fused collaborative attention network MASR, which sufficiently learns the temporal and positional features of nodes, and innovatively measures the importance of these two features for analyzing the nodes’ dynamic patterns. In addition, we incorporate semantic-enriched contrastive learning into collaborative filtering to enhance the semantic association between nodes and reduce the noise from the structural neighborhood, which has a positive effect on the sequential recommendation. Compared with the baseline models, the performance of MASR on MovieLens, CDs and Beauty datasets is improved by 2.0%, 2.1% and 1.7% respectively, proving its effectiveness in the sequential recommendation. 相似文献
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A critical challenge for Web search engines concerns how they present relevant results to searchers. The traditional approach is to produce a ranked list of results with title and summary (snippet) information, and these snippets are usually chosen based on the current query. Snippets play a vital sensemaking role, helping searchers to efficiently make sense of a collection of search results, as well as determine the likely relevance of individual results. Recently researchers have begun to explore how snippets might also be adapted based on searcher preferences as a way to better highlight relevant results to the searcher. In this paper we focus on the role of snippets in collaborative web search and describe a technique for summarizing search results that harnesses the collaborative search behaviour of communities of like-minded searchers to produce snippets that are more focused on the preferences of the searchers. We go on to show how this so-called social summarization technique can generate summaries that are significantly better adapted to searcher preferences and describe a novel personalized search interface that combines result recommendation with social summarization. 相似文献
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This paper addresses the problem of the automatic recognition and classification of temporal expressions and events in human language. Efficacy in these tasks is crucial if the broader task of temporal information processing is to be successfully performed. We analyze whether the application of semantic knowledge to these tasks improves the performance of current approaches. We therefore present and evaluate a data-driven approach as part of a system: TIPSem. Our approach uses lexical semantics and semantic roles as additional information to extend classical approaches which are principally based on morphosyntax. The results obtained for English show that semantic knowledge aids in temporal expression and event recognition, achieving an error reduction of 59% and 21%, while in classification the contribution is limited. From the analysis of the results it may be concluded that the application of semantic knowledge leads to more general models and aids in the recognition of temporal entities that are ambiguous at shallower language analysis levels. We also discovered that lexical semantics and semantic roles have complementary advantages, and that it is useful to combine them. Finally, we carried out the same analysis for Spanish. The results obtained show comparable advantages. This supports the hypothesis that applying the proposed semantic knowledge may be useful for different languages. 相似文献
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随着移动互联网的发展,越来越多的用户信息获取过程通过移动终端完成.但当前个性化推荐系统对用户情境的感知能力不足,缺乏为用户提供符合当前情境的个性化信息推荐服务.为此,本文提出了基于贝叶斯方法的情境化用户资源类别偏好学习以及融合该类别偏好的协同过滤个性化信息推荐.运用贝叶斯方法学习用户在不同情境下对各资源类别的偏好,然后将该类别偏好与传统协同过滤推荐算法相结合,生成符合用户当前情境的个性化信息推荐.实验表明本文提出的改进算法可以提高推荐的准确率. 相似文献
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《Information processing & management》2022,59(6):103087
In producing news stories, journalists depend on information obtained from sources. This paper reviews the literature on journalists’ information seeking. The 90 studies included in the review cover how journalists identify sources, interact with sources, interpret information, and manage sources. In addition to quality and accessibility, balance in the group of sources selected is an important criterion in journalists’ identification of sources. However, the importance journalists assign to balance stands in contrast to the frequent finding of bias in their source selections. In interactions with sources, the sources frequently provide ideas for new stories in addition to information for current ones. This finding shows how multiple instances of information seeking coexist and combine into a mesh of intersecting information-seeking processes. In interpreting information, journalists are acutely aware that sources may have an agenda or be misinformed. While journalists praise information checking, they regularly bypass it or replace direct checks for information quality with indirect checks, such as whether the source appears trustworthy. In managing sources, journalists engage in boundary work to regulate their relationship with sources. They also cultivate long-term relationships with selected sources. The review findings are discussed with respect to how journalism shapes journalists’ information seeking and what implications the findings have for information-behavior research in other domains. 相似文献
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We consider a network of autonomous peers forming a logically global but physically distributed search engine, where every peer has its own local collection generated by independently crawling the Web. A challenging task in such systems is to efficiently route user queries to peers that can deliver high quality results and be able to rank these returned results, thus satisfying the users’ information need. However, the problem inherent with this scenario is selecting a few promising peers out of an a priori unlimited number of peers. In recent research a rather strict notion of semantic overlay networks has been established. In most approaches, peers are connected to other peers based on a rigid semantic profile by clustering them based on their contents. In contrast, our strategy follows the spirit of peer autonomy and creates semantic overlay networks based on the notion of “peer-to-peer dating”. Peers are free to decide which connections they create and which they want to avoid based on various usefulness estimators. The proposed techniques can be easily integrated into existing systems as they require only small additional bandwidth consumption as most messages can be piggybacked onto established communication. We show how we can greatly benefit from these additional semantic relations during query routing in search engines, such as Minerva, and in the JXP algorithm, which computes the PageRank authority measure in a completely decentralized manner. 相似文献
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Fatemeh Lashkari Ebrahim Bagheri Ali A. Ghorbani 《Information processing & management》2019,56(3):733-755
Traditional information retrieval techniques that primarily rely on keyword-based linking of the query and document spaces face challenges such as the vocabulary mismatch problem where relevant documents to a given query might not be retrieved simply due to the use of different terminology for describing the same concepts. As such, semantic search techniques aim to address such limitations of keyword-based retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic information might not lead to improved retrieval performance over keyword-based search, their consideration enables the retrieval of a set of relevant documents that cannot be retrieved by keyword-based methods. As such, building indices that store and provide access to semantic information during the retrieval process is important. While the process for building and querying keyword-based indices is quite well understood, the incorporation of semantic information within search indices is still an open challenge. Existing work have proposed to build one unified index encompassing both textual and semantic information or to build separate yet integrated indices for each information type but they face limitations such as increased query process time. In this paper, we propose to use neural embeddings-based representations of term, semantic entity, semantic type and documents within the same embedding space to facilitate the development of a unified search index that would consist of these four information types. We perform experiments on standard and widely used document collections including Clueweb09-B and Robust04 to evaluate our proposed indexing strategy from both effectiveness and efficiency perspectives. Based on our experiments, we find that when neural embeddings are used to build inverted indices; hence relaxing the requirement to explicitly observe the posting list key in the indexed document: (a) retrieval efficiency will increase compared to a standard inverted index, hence reduces the index size and query processing time, and (b) while retrieval efficiency, which is the main objective of an efficient indexing mechanism improves using our proposed method, retrieval effectiveness also retains competitive performance compared to the baseline in terms of retrieving a reasonable number of relevant documents from the indexed corpus. 相似文献
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《Information processing & management》2023,60(3):103321
Social question-and-answer (Q&A) sites have the potential to serve as a useful source of online information based on their content-focused and collaborative nature. Although previous research has examined various attributes of high-quality information on social Q&A sites (e.g., best answers), relatively less attention has been paid to what affects users’ credibility assessments of information in the social Q&A context. The present study developed a social Q&A platform-specific framework for web credibility assessment, including 21 criteria under six types of web credibility, based on a literature analysis and case study of two online Q&A communities, Stack Exchange and Wikipedia Reference Desk. Using the selected sites’ policies and guidelines (n = 46) as the source of evidence, the case study revealed that content-related attributes (e.g., evidence-based, pertinence) were most frequently identified (12 of 21 criteria) as potential cues and heuristics for web credibility assessments of social Q&A sites, followed by author-related (five of 21; e.g., reputation) and design-related (four of 21; e.g., engaging design) factors. Design-related criteria were rarely included in previous models of web credibility on social Q&A or similar peer-knowledge production platforms. However, our findings showing that both Stack Exchange and Wikipedia Reference Desk have policies regarding all four design-related criteria in our framework—engaging design, moderation, design appropriateness, and ease of use—indicate the potential influences of design features on users’ web credibility assessment on social Q&A sites. Some differences emerged between the two cases, such as policies regarding the answerer's credentials or semantic accuracy that are present on Wikipedia Reference Desk but absent on Stack Exchange. Such differences in the sites’ policies reflect how they position themselves as social Q&A communities—Wikipedia, of which Wikipedia Reference Desk is a part, as an encyclopedia, and Stack Exchange as a community-based platform for learning, sharing knowledge, and building careers of users. 相似文献
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《Information processing & management》2023,60(2):103145
Machine reading comprehension (MRC) is a challenging task in the field of artificial intelligence. Most existing MRC works contain a semantic matching module, either explicitly or intrinsically, to determine whether a piece of context answers a question. However, there is scant work which systematically evaluates different paradigms using semantic matching in MRC. In this paper, we conduct a systematic empirical study on semantic matching. We formulate a two-stage framework which consists of a semantic matching model and a reading model, based on pre-trained language models. We compare and analyze the effectiveness and efficiency of using semantic matching modules with different setups on four types of MRC datasets. We verify that using semantic matching before a reading model improves both the effectiveness and efficiency of MRC. Compared with answering questions by extracting information from concise context, we observe that semantic matching yields more improvements for answering questions with noisy and adversarial context. Matching coarse-grained context to questions, e.g., paragraphs, is more effective than matching fine-grained context, e.g., sentences and spans. We also find that semantic matching is helpful for answering who/where/when/what/how/which questions, whereas it decreases the MRC performance on why questions. This may imply that semantic matching helps to answer a question whose necessary information can be retrieved from a single sentence. The above observations demonstrate the advantages and disadvantages of using semantic matching in different scenarios. 相似文献
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《Information processing & management》2020,57(2):102058
Recommender systems are based on inherent forms of social influence. Indeed, suggestions are provided to the users based on the opinions of peers. Given the relevance that ratings have nowadays to push the sales of an item, sellers might decide to bribe users so that they rate or change the ratings given to items, thus increasing the sellers’ reputation. Hence, by exploiting the fact that influential users can lead an item to get recommended, bribing can become an effective way to negatively exploit social influence and introduce a bias in the recommendations. Given that bribing is forbidden but still employed by sellers, we propose a novel matrix completion algorithm that performs hybrid memory-based collaborative filtering using an approximation of Kolmogorov complexity. We also propose a framework to study the bribery effect and the bribery resistance of our approach. Our theoretical analysis, validated through experiments on real-world datasets, shows that our approach is an effective way to counter bribing while, with state-of-the-art algorithms, sellers can bribe a large part of the users. 相似文献
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《Information processing & management》2023,60(5):103421
Semantic information in judgement documents has been an important source in Artificial Intelligence and Law. Sequential representation is the traditional structure for analyzing judgement documents and supporting the legal charge prediction task. The main problem is that it is not effective to represent the criminal semantic information. In this paper, to represent and verify the criminal semantic information such as multi-linked legal features, we propose a novel criminal semantic representation model, which constructs the Criminal Action Graph (CAG) by extracting criminal actions linked in two temporal relationships. Based on the CAG, a Graph Convolutional Network is also adopted as the predictor for legal charge prediction. We evaluate the validity of CAG on the confusing charges which composed of 32,000 judgement documents on five confusing charge sets. The CAG reaches about 88% accuracy averagely, more than 3% over the compared model. The experimental standard deviation also show the stability of our model, which is about 0.0032 on average, nearly 0. The results show the effectiveness of our model for representing and using the semantic information in judgement documents. 相似文献
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As access to information becomes more intensive in society, a great deal of that information is becoming available through diverse channels. Accordingly, users require effective methods for accessing this information. Conversational agents can act as effective and familiar user interfaces. Although conversational agents can analyze the queries of users based on a static process, they cannot manage expressions that are more complex. In this paper, we propose a system that uses semantic Bayesian networks to infer the intentions of the user based on Bayesian networks and their semantic information. Since conversation often contains ambiguous expressions, the managing of context and uncertainty is necessary to support flexible conversational agents. The proposed method uses mixed-initiative interaction (MII) to obtain missing information and clarify spurious concepts in order to understand the intention of users correctly. We applied this to an information retrieval service for websites to verify the usefulness of the proposed method. 相似文献
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《Information processing & management》2022,59(1):102743
Multi-modal hashing can encode the large-scale social geo-media multimedia data from multiple sources into a common discrete hash space, in which the heterogeneous correlations from multiple modalities could be well explored and preserved into the objective semantic-consistent hash codes. The current researches on multi-modal hashing mainly focus on performing common data reconstruction, but they fail to effectively distill the intrinsic and consensus structures of multi-modal data and fully exploit the inherent semantic knowledge to capture semantic-consistent information across multiple modalities, leading to unsatisfactory retrieval performance. To facilitate this problem and develop an efficient multi-modal geographical retrieval method, in this article, we propose a discriminative multi-modal hashing framework named Cognitive Multi-modal Consistent Hashing (CMCH), which can progressively pursue the structure consensus over heterogeneous multi-modal data and simultaneously explore the informative transformed semantics. Specifically, we construct a parameter-free collaborative multi-modal fusion module to incorporate and excavate the underlying common components from multi-source data. Particularly, our formulation seeks for a joint multi-modal compatibility among multiple modalities under a self-adaptive weighting manner, which can take full advantages of their complementary properties. Moreover, a cognitive self-paced learning policy is further leveraged to conduct progressive feature aggregation, which can coalesce multi-modal data onto the established common latent space in a curriculum learning mode. Furthermore, deep semantic transform learning is elaborated to generate flexible semantics for interactively guiding collaborative hash codes learning. An efficient discrete learning algorithm is devised to address the resulting optimization problem, which obtains stable solutions when dealing with large-scale multi-modal retrieval tasks. Sufficient experiments performed on four large-scale multi-modal datasets demonstrate the encouraging performance of the proposed CMCH method in comparison with the state-of-the-arts over multi-modal information retrieval and computational efficiency. The source codes of this work could be available at https://github.com/JunfengAn1998a/CMCH . 相似文献
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Fattane Zarrinkalam Mohsen Kahani Ebrahim Bagheri 《Information processing & management》2018,54(2):339-357
Inferring users’ interests from their activities on social networks has been an emerging research topic in the recent years. Most existing approaches heavily rely on the explicit contributions (posts) of a user and overlook users’ implicit interests, i.e., those potential user interests that the user did not explicitly mention but might have interest in. Given a set of active topics present in a social network in a specified time interval, our goal is to build an interest profile for a user over these topics by considering both explicit and implicit interests of the user. The reason for this is that the interests of free-riders and cold start users who constitute a large majority of social network users, cannot be directly identified from their explicit contributions to the social network. Specifically, to infer users’ implicit interests, we propose a graph-based link prediction schema that operates over a representation model consisting of three types of information: user explicit contributions to topics, relationships between users, and the relatedness between topics. Through extensive experiments on different variants of our representation model and considering both homogeneous and heterogeneous link prediction, we investigate how topic relatedness and users’ homophily relation impact the quality of inferring users’ implicit interests. Comparison with state-of-the-art baselines on a real-world Twitter dataset demonstrates the effectiveness of our model in inferring users’ interests in terms of perplexity and in the context of retweet prediction application. Moreover, we further show that the impact of our work is especially meaningful when considered in case of free-riders and cold start users. 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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Human collaborative relationship inference is a meaningful task for online social networks and is called link prediction in network science. Real-world networks contain multiple types of interacting components and can be modeled naturally as heterogeneous information networks (HINs). The current link prediction algorithms in HINs fail to effectively extract training samples from snapshots of HINs; moreover, they underutilise the differences between nodes and between meta-paths. Therefore, we propose a meta-circuit machine (MCM) that can learn and fuse node and meta-path features efficiently, and we use these features to inference the collaborative relationships in question-and-answer and bibliographic networks. We first utilise meta-circuit random walks to obtain training samples in which the basic idea is to perform biased meta-path random walks on the input and target network successively and then connect them. Then, a meta-circuit recurrent neural network (mcRNN) is designed for link prediction, which represents each node and meta-path by a dense vector and leverages an RNN to fuse the features of node sequences. Experiments on two real-world networks demonstrate the effectiveness of our framework. This study promotes the investigation of potential evolutionary mechanisms for collaborative relationships and offers practical guidance for designing more effective recommendation systems for online social networks. 相似文献
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《Information processing & management》2020,57(6):102310
This paper presents a novel genetic-based recommender system (BLIGA) that depends on the semantic information and historical rating data. The main contribution of this research lies in evaluating the possible recommendation lists instead of evaluating items then forming the recommendation list. BLIGA utilizes the genetic algorithm to find the best list of items to the active user. Thus, each individual represents a candidate recommendation list. BLIGA hierarchically evaluates the individuals using three fitness functions. The first function uses semantic information about items to estimates the strength of the semantic similarity between items. The second function estimates the similarity in satisfaction level between users. The third function depends on the predicted ratings to select the best recommendation list.BLIGA results have been compared against recommendation results from alternative collaborative filtering methods. The results demonstrate the superiority of BLIGA and its capability to achieve more accurate predictions than the alternative methods regardless of the number of K-neighbors. 相似文献
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针对当前高校学科信息服务平台存在的对服务对象信息需求挖掘、分析不足的弊端,提出构建基于协同过滤算法的学科信息服务平台。通过引入读者专业、角色、学历、借阅记录等影响和反映读者信息需求的因素构建读者特征模型,该模型采用优化的协同过滤算法挖掘读者信息需求并产生个性化推荐信息,可有效提升学科信息服务质量。 相似文献