Venue Recommendation Research Articles

A hybrid personalized scholarly venue recommender system integrating social network analysis and contextual similarity

Rapidly developing academic venues throw a challenge to researchers in identifying the most appropriate ones that are in-line with their scholarly interests and of high relevance. Even a high-quality paper is sometimes rejected due to a mismatch between the area of the paper, and the scope of the journal attempted to. Recommending appropriate academic venues can, therefore, enable researchers to identify and take part in relevant conferences and to publish in impactful journals. Although a researcher may know a few leading high-profile venues for her specific field of interest, a venue recommender system becomes particularly helpful when one explores a new field or when more options are needed. We propose DISCOVER: A Diversified yet Integrated Social network analysis and COntextual similarity-based scholarly VEnue Recommender system. Our work provides an integrated framework incorporating social network analysis, including centrality measure calculation, citation and co-citation analysis, topic modeling based contextual similarity, and key-route identification based main path analysis of a bibliographic citation network. The paper also addresses cold start issues for a new researcher and a new venue along with a considerable reduction in data sparsity, computational costs, diversity, and stability problems. Experiments based on the Microsoft Academic Graph (MAG) dataset show that the proposed DISCOVER outperforms state-of-the-art recommendation techniques using standard metrics of precision@k, nDCG@k, accuracy, MRR, F−measuremacro, diversity, stability, and average venue quality.

Handling uncertainty in social media textual information for improving venue recommendation formulation quality in social networks

One of the major problems that social media front is to continuously produce successful, user-targeted information, in the form of recommendations, which are produced by applying methods from the area of recommender systems. One of the most important applications of recommender systems in social networks is venue recommendation, targeted by the majority of the leading social networks (Facebook, TripAdvisor, OpenTable, etc.). However, recommender systems’ algorithms rely only on the existence of numeric ratings which are typically entered by users, and in the context of social networks, this information is scarce, since many social networks allow only reviews, rather than explicit ratings. Even if explicit ratings are supported, users may still resort to expressing their views and rating their experiences through submitting posts, which is the predominant user practice in social networks, rather than entering explicit ratings. User posts contain textual information, which can be exploited to compute derived ratings, and these derived ratings can be used in the recommendation process in the lack of explicitly entered ratings. Emerging recommender systems encompass this approach, without however tackling the fact that the ratings computed on the basis of textual information may be inaccurate, due to the very nature of the computation process. In this paper, we present an approach which extracts features of the textual information, a widely available source of information in venue category, to compute a confidence metric for the ratings that are computed from texts; then, this confidence metric is used in the user similarity computation and venue rating prediction formulation process, along with the computed rating. Furthermore, we propose a venue recommendation method that considers the generated venue rating predictions, along with venue QoS, similarity and spatial distance metrics in order to generate venue recommendations for social network users. Finally, we validate the accuracy of the rating prediction method and the user satisfaction from the recommendations generated by the recommendation formulation algorithm. Conclusively, the introduction of the confidence level significantly improves rating prediction accuracy, leverages the ability to generate personalized recommendations for users and increases user satisfaction.

CNAVER: A Content and Network-based Academic VEnue Recommender system

The phenomenon of rapidly developing academic venues poses a significant challenge for researchers: how to recognize the ones that are not only in accordance with one’s scholarly interests but also of high significance? Often, even a high-quality paper is rejected because of a mismatch between the research area of the paper and the scope of the journal. Recommending appropriate scholarly venues to researchers empowers them to recognize and partake in important academic conferences and assists them in getting published in impactful journals. A venue recommendation system becomes helpful in this scenario, particularly when exploring a new field or when further choices are required. We propose CNAVER: A Content and Network-based Academic VEnue Recommender system. It provides an integrated framework employing a rank-based fusion of paper-paper peer network (PPPN) model and venue-venue peer network (VVPN) model. It only requires the title and abstract of a paper to provide venue recommendations, thus assisting researchers even at the earliest stage of paper writing. It also addresses cold start issues such as the involvement of an inexperienced researcher and a novel venue along with the problems of data sparsity, diversity, and stability. Experiments on the DBLP dataset exhibit that our proposed approach outperforms several state-of-the-art methods in terms of precision, nDCG, MRR, accuracy, F−measuremacro, average venue quality, diversity, and stability.

GeoLifecycle

As Location-Based Social Networks (LBSNs) have become widely used by users, understanding user engagement and predicting user churn are essential to the maintainability of the services. In this work, we conduct a quantitative analysis to understand user engagement patterns exhibited both offline and online in LBSNs. We employ two large-scale datasets which consist of 1.3 million and 62 million users with 5.3 million reviews and 19 million tips in Yelp and Foursquare, respectively. We discover that users keep traveling to diverse locations where they have not reviewed before, which is in contrast to "human life" analogy in real life, an initial exploration followed by exploitation of existing preferences. Interestingly, we find users who eventually leave the community show distinct engagement patterns even with their first ten reviews in various facets, e.g., geographical, venue-specific, linguistic, and social aspects. Based on these observations, we construct predictive models to detect potential churners. We then demonstrate the effectiveness of our proposed features in the churn prediction. Our findings of geographical exploration and online interactions of users enhance our understanding of human mobility based on reviews, and provide important implications for venue recommendations and churn prediction.

A Contextual Recurrent Collaborative Filtering framework for modelling sequences of venue checkins

Context-Aware Venue Recommendation (CAVR) systems aim to effectively generate a ranked list of interesting venues users should visit based on their historical feedback (e.g. checkins) and context (e.g. the time of the day or the user’s current location). Such systems are increasingly deployed by Location-based Social Networks (LBSNs) such as Foursquare and Yelp to enhance the satisfaction of the users. Matrix Factorisation (MF) is a popular Collaborative Filtering (CF) technique that can suggest relevant venues to users based on an assumption that similar users are likely to visit similar venues. In recent years, deep neural networks have been successfully applied to recommendation systems. Indeed, various approaches have been previously proposed in the literature to enhance the effectiveness of MF-based approaches by exploiting Recurrent Neural Networks (RNN) models to capture the sequential properties of observed checkins. Moreover, recently, several RNN architectures have been proposed to incorporate contextual information associated with the users’ sequence of checkins (for instance, the time interval or the geographical distance between two successive checkins) to effectively capture such short-term preferences of users. In this work, we propose a Contextual Recurrent Collaborative Filtering framework (CRCF) that leverages the users’ preferred context and the contextual information associated with the users’ sequence of checkins in order to model the users’ short-term preferences for CAVR. In particular, the CRCF framework is built upon two state-of-the-art approaches: namely Deep Recurrent Collaborative Filtering framework (DRCF) and Contextual Attention Recurrent Architecture (CARA). Thorough experiments on three large checkin and rating datasets from commercial LBSNs demonstrate the effectiveness and robustness of our proposed CRCF framework by significantly outperforming various state-of-the-art matrix factorisation approaches. In particular, the CRCF framework significantly improves NDCG@10 by 5–20% over the state-of-the-art DRCF framework (Manotumruksa, Macdonald, and Ounis, 2017a) and the CARA architecture (Manotumruksa, Macdonald, and Ounis, 2018) across the three datasets. Furthermore, the CRCF framework is less significantly risky than both the DRCF framework and the CARA architecture across the three datasets.

Measuring the Significance of Writing Style for Recommending Where to Publish – A Case Study

Abstract Writing style plays a role in publication venue recommendation. However, such finding should be observed further; it is concluded from an arbitrary dataset which contains various topics and writing quality. This paper aims to observe that style's impact in a more controlled environment. A dataset with the same specific topic and writing quality was used and analysed. In our case, the dataset is comprised of papers published on reputable software engineering publication venues with natural language generation as the specific topic. According to our observation, writing style only affects significantly on paper title wherein the impact is proportional to n in n-gram. Moreover, the style’s impact becomes more salient when the venues are grouped per publisher or only a specific publication type is considered.

ROVETS

As a result of the tremendous proliferation of sessions at academic conferences, recommending appropriate venues for researchers has become a considerable problem. In this article, the authors propose an innovative recommender algorithm called Recommendation of Venues and Environments Through Social-Awareness (ROVETS). ROVETS seeks to enhance the social awareness of attendees at a smart conference. ROVETS initially employs closeness centrality and Breadth First Search (BFS) to detect potential presenters for a target attendee. Then, the accurate tie strength between the attendees and presenters as well as the degree centrality of the presenters are computed based on similarity of their research interests. Using the computations above, ROVETS generates effective recommendations pertaining to venues for attendees who have high tie strength with presenters. Through a relevant real-world dataset, this article evaluates the proposed recommender algorithm. These experimental results validate that ROVETS exhibits favorable enhancements over other existing state-of-the-art methods.

The Deep Learning-Based Recommender System "Pubmender" for Choosing a Biomedical Publication Venue: Development and Validation Study.

BackgroundIt is of great importance for researchers to publish research results in high-quality journals. However, it is often challenging to choose the most suitable publication venue, given the exponential growth of journals and conferences. Although recommender systems have achieved success in promoting movies, music, and products, very few studies have explored recommendation of publication venues, especially for biomedical research. No recommender system exists that can specifically recommend journals in PubMed, the largest collection of biomedical literature.ObjectiveWe aimed to propose a publication recommender system, named Pubmender, to suggest suitable PubMed journals based on a paper’s abstract.MethodsIn Pubmender, pretrained word2vec was first used to construct the start-up feature space. Subsequently, a deep convolutional neural network was constructed to achieve a high-level representation of abstracts, and a fully connected softmax model was adopted to recommend the best journals.ResultsWe collected 880,165 papers from 1130 journals in PubMed Central and extracted abstracts from these papers as an empirical dataset. We compared different recommendation models such as Cavnar-Trenkle on the Microsoft Academic Search (MAS) engine, a collaborative filtering–based recommender system for the digital library of the Association for Computing Machinery (ACM) and CiteSeer. We found the accuracy of our system for the top 10 recommendations to be 87.0%, 22.9%, and 196.0% higher than that of MAS, ACM, and CiteSeer, respectively. In addition, we compared our system with Journal Finder and Journal Suggester, which are tools of Elsevier and Springer, respectively, that help authors find suitable journals in their series. The results revealed that the accuracy of our system was 329% higher than that of Journal Finder and 406% higher than that of Journal Suggester for the top 10 recommendations. Our web service is freely available at https://www.keaml.cn:8081/.ConclusionsOur deep learning–based recommender system can suggest an appropriate journal list to help biomedical scientists and clinicians choose suitable venues for their papers.

Representing anything from scholar papers

Many scholar knowledge mining tasks depend on the representations of different scholar entities and their compositions, including author, publication venue and paper. However, the existing methods always tried to learn different kinds of scholar representations individually. The citation relationships among different types of entities are sometimes ignored. Moreover, the separated learned representations are in separate vector space and hence cannot be operated together. Indeed, representations for different kinds of entities are constructed based on the papers and the citation relationships, which provide the opportunity for developing an unified representation method. In this paper, we propose a novel method for mapping scholar entities and their compositions to a unified distributed vector space. Each paper is firstly defined as a full composition of entities, including title, author, publication venue and so on. Then, with the citation relationships among papers, we train a model that tries to maximize the likelihood of references of papers. After training, the model can map all kinds of entities and their compositions to a unified vector space. We quantitatively evaluate our vectors on two tasks of collaborate prediction and related paper recommendation. The results show our method can learn good representations for scholar entities and their compositions. Since our method allows different types of representations to be operated together, we also show some interesting applications, such as publication venue recommendation and reviewer recommendation.

GEVR

In this paper, we present GEVR, the first Group Event Venue Recommendation system that incorporates mobility via individual location traces and context information into a "social-based" group decision model to provide venue recommendations for groups of mobile users. Our study leverages a real-world dataset collected using the OutWithFriendz mobile app for group event planning, which contains 625 users and over 500 group events. We first develop a novel "social-based" group location prediction model, which adaptively applies different group decision strategies to groups with different social relationship strength to aggregate each group member's location preference, to predict where groups will meet. Evaluation results show that our prediction model not only outperforms commonly used and state-of-the-art group decision strategies with over 80% accuracy for predicting groups' final meeting location clusters, but also provides promising qualities in cold-start scenarios. We then integrate our prediction model with the Foursquare Venue Recommendation API to construct an event venue recommendation framework for groups of mobile users. Evaluation results show that GEVR outperforms the comparative models by a significant margin.

Academic Venue Recommendations Based on Similarity Learning of an Extended Nearby Citation Network

The rapidly increasing number of potential academic venues for research publication and commentary has made sourcing the venue that would best contribute to promoting effective scientific cooperation more challenging. In this paper, we propose a similarity learning approach to determine the most appropriate venue to publish an article. We first analyze the article metadata and cited articles to build the citation network matrices of the given article and then apply these to learn and build similarity matrices between academic objects (i.e., articles, authors, and venues) at an extended nearby article citation network. Using the formed matrices, we estimate a collaborative anticipation confidence score of a relationship between the venues in the extended network. For our empirical studies, we used an actual academic dataset to validate the efficiency of our approach and recommend an appropriate academic venue. The experimental results highlight the effectiveness of our proposed approach to optimize overall recommendation quality, compared with other baseline approaches.

A real-time social network-based knowledge discovery system for decision making

ABSTRACTThe increasing amount of data in social networks has complicated data processing and interpretation. Therefore, intelligent decision-support mechanisms that have the ability to automatically extract meaning from data and interpret the opinions of people in real time have become inevitable. In this study, an intelligent multilingual decision support system was implemented, and a new algorithm that employs text mining and sentiment analysis techniques was developed to automatically interpret the opinions of social network users about the places they plan to visit. The system can be used as a baseline for sentiment analysis in social networks and can be adapted to build new systems. In this study, we set our main focus on Turkish language and show the applicability of our approach for other languages through the experiments for English language. The dataset required for the implementation of text mining techniques was created based on the venue recommendations shared on Foursquare social media platform. As a result, a contribution was made to the way the social network users make decisions without reading thousands of recommendations. Our results show that the developed system achieves classification accuracy of 84.49% for Turkish and 95% for English. Finally, the most liked or disliked foods/beverages are correctly identified for 107 out of 128 venues.

HybRecSys: Content-based contextual hybrid venue recommender system

The popularity of location-based social networks has prompted researchers to study recommendation systems for location-based services. When used separately, each existing venue recommendation system algorithm has its own drawbacks (e.g. cold start, data sparsity, scalability). Another issue is that critical information about context is not commonly used in venue recommendation systems. This article proposes a hybrid recommendation model that combines contextual information, user-based and item-based collaborative filtering and content-based filtering. For this purpose, we collected user visit histories, venue-related information (distance, category, popularity and price) and contextual information (weather, season, date and time of visits) related to individual user visits from Twitter, Foursquare and Weather Underground. Experimental evaluation of the proposed hybrid system (HybRecSys) using a real-world dataset shows better results than baseline approaches.

PAVE: Personalized Academic Venue recommendation Exploiting co-publication networks

Academic venues have risen beyond the imagination for the rapid development of information technology. It is necessary for researchers to acknowledge high quality and fruitful academic venues. However, the information overload problem in big scholarly data creates tremendous challenges for mining these venues and relevant information. In this work, we propose PAVE, a novel Personalized Academic Venue recommendation Exploiting co-publication networks. PAVE runs a random walk with restart model on a co-publication network which contains two kinds of associations, coauthor relations and author-venue relations. We define a transfer matrix with bias to drive the random walk by exploiting three academic factors, co-publication frequency, relation weight and researchers' academic level. PAVE is inspired from the fact that researchers are more likely to contact those who have high co-publication frequencies and similar academic levels. Additionally, in PAVE, we consider the difference of weights between two kinds of associations. Extensive experiments on DBLP data set demonstrate that, in comparison to relevant baseline approaches, PAVE performs better in terms of precision, recall, F1 and average venue quality.

Exploiting Internet of Things information to enhance venues’ recommendation accuracy

In this paper, we introduce a novel recommendation algorithm, which exploits data sourced from web services provided by the Internet of Things in order to produce more accurate venue recommendations. The proposed algorithm provides added value for the web services offered by the Internet of Things and enhances the state-of-the-art in this algorithm category by taking into account (a) web of things data regarding the contexts of the user and the context of the venues to be recommended (restaurants, movie theaters, etc.), such as the user’s geographical position, road traffic and weather conditions, (b) qualitative aspects of the venues, such as price, atmosphere or service, (c) the semantic similarity of venues and (d) the influencing factors between social network users, derived from user participation in social networks. The combination of these features leads to more accurate and better user-targeted recommendations. We also present a framework which incorporates the above characteristics, and we evaluate the presented algorithm, both in terms of performance and recommendation quality.

VRer: Context-Based Venue Recommendation using embedded space ranking SVM in location-based social network

Venue recommendation has attracted a lot of research attention with the rapid development of Location-Based Social Networks. The effectiveness of venue recommendation largely depends on how well it captures users’ contexts or preferences. However, it is quite difficult, if not impossible, to capture the whole information about users’ preferences. In addition, users’ preferences are often heterogeneous (i.e., some preferences are static and common to all users while some preferences are dynamic and diverse). Existing venue recommendation does not well address the aforementioned issues and often recommends the most popular, the cheapest, or the closest venues based on simple contexts.In this paper, we cast the venue recommendation as a ranking problem and propose a recommendation framework named VRer (Context-Based Venue Recommendation using embedded space ranking SVM) employing an embedded space ranking SVM model to separate the venues in terms of different characteristics. Our proposed approach makes use of ‘check-in’ data to capture users’ preferences and utilizes a machine learning model to tune the importance of different factors in ranking. The major contribution of this paper are: (1) VRer combines various contexts (e.g., the temporal influence and the category of locations) with the check-in records to capture individual heterogeneous preferences; (2) we propose an embedded space ranking SVM optimizing the learning function to reduce the time consumption of training the personalized recommendation model for each group or user; (3) we evaluate our proposed approach against a real world LBSN and compare it with other baseline methods. Experimental results demonstrate the benefits of our proposed approach.

Recommendation of scholarly venues based on dynamic user interests

The ever-growing number of venues publishing academic work makes it difficult for researchers to identify venues that publish data and research most in line with their scholarly interests. A solution is needed, therefore, whereby researchers can identify information dissemination pathways in order to both access and contribute to an existing body of knowledge. In this study, we present a system to recommend scholarly venues rated in terms of relevance to a given researcher's current scholarly pursuits and interests. We collected our data from an academic social network and modeled researchers' scholarly reading behavior in order to propose a new and adaptive implicit rating technique for venues. We present a way to recommend relevant, specialized scholarly venues using these implicit ratings that can provide quick results, even for new researchers without a publication history and for emerging scholarly venues that do not yet have an impact factor. We performed a large-scale experiment with real data to evaluate the current scholarly recommendation system and showed that our proposed system achieves better results than the baseline. The results provide important up-to-the-minute signals that compared with post-publication usage-based metrics represent a closer reflection of a researcher's interests.

CRATS: An LDA-Based Model for Jointly Mining Latent Communities, Regions, Activities, Topics, and Sentiments from Geosocial Network Data

Geosocial networks like Yelp and Foursquare have been rapidly growing and accumulating plenty of data such as social links between users , user check-ins to venues , venue geographical locations , venue categories , and user textual comments on venues . These data contain rich knowledge on the user's social interactions in communities, geographical mobility patterns between regions, categorical preferences on activities, aspect interests in topics, and opinion expressions for sentiments. Such knowledge is essential for two key applications, namely, text sentiment classification and venue recommendations, which will be developed in this paper. To extract the knowledge from the data, the key task is to discover the latent communities , regions , activities , topics , and sentiments of users. However, these latent variables are interdependent, e.g., users in the same community usually travel on nearby regions and share common activities and topics, which renders a big challenge for modeling these latent variables. To tackle this challenge, in this study, we propose an LDA-based model called CRATS that jointly mines the latent Communities, Regions, Activities, Topics, and Sentiments based on the important dependencies among these latent variables. To the best of our knowledge, this is the first study to jointly model these five latent variables. Finally, we conduct a comprehensive performance evaluation for CRATS in different applications, including text sentiment classification and venue recommendations, using three large-scale real-world geosocial network data sets collected from Yelp and Foursquare. Experimental results show that CRATS achieves significantly superior performance against other state-of-the-art techniques.

Cross-Platform Multi-Modal Topic Modeling for Personalized Inter-Platform Recommendation

In this paper, we investigate a novel cross- platform multimedia problem: given two platforms, Flickr and Foursquare, we conduct the recommendation between these two platforms, namely the photo recommendation from Flickr to Foursquare users and the venue recommendation from Foursquare to Flickr users. Such inter-platform recommendations enable users from one single platform to enjoy different recommendation services effectively . To solve the problem, we propose a cross- platform multi-modal topic model ( ${\rm CM}^3{\rm TM}$ ), which is capable of: 1) differentiating between two kinds of topics, i.e., platform- specific topics only relevant to a certain platform and shared topics characterizing the knowledge shared by different platforms and 2) aligning multiple modalities from different platforms. Specifically, ${\rm CM}^3{\rm TM}$ can not only split the topic space into the shared topic space and platform-specific topic space and learn them simultaneously, but also enable the alignment among different modalities through the learned topic space. Given the location information, we applied the proposed ${\rm CM}^3{\rm TM}$ into two inter-platform recommendation applications: 1) personalized venue recommendation from Foursquare to Flickr users and 2) personalized image recommendation from Flickr to Foursquare users. We have conducted experiments on the collected large-scale real-world dataset from Flickr and Foursquare. Qualitative and quantitative evaluation results validate the effectiveness of our method and demonstrate the advantage of connecting different platforms with different modalities for the inter-platform recommendation.

Quality models for venue recommendation in location-based social network

With the rapid development of location-based social networks (LBSNs), increasing media data is ceaselessly uploaded by users. The multimedia data is often scattered and not informative and conseque...