Venue Recommendation System Research Articles

Where should I publish? Heterogeneous, networks-based prediction of paper’s citation success

• Representing papers as embeddings of decomposed publication network’s modes. • Prediction of a paper’s future citation success using publication-day data. • Low-rank Comp Sci journals may bring better citation success than high-ranked ones. • Basis for a future venue-recommendation system. Scientific output, as measured in research published annually, has seen a consistent growth for decades now. As more manuscripts are submitted for publication each year, new publishing venues appear – often as increasingly specialised offshoots of existing journals and conferences. This situation presents scholars with a wealth of publishing venues to consider and choose from for their manuscripts. Surprisingly, we find that the most cited papers are not necessarily found in the highest-ranked journals, while the best conferences dominate in this regard. We find it intriguing that popular Computer Science conferences act like a vacuum of attention, centralising all good publications, while journals are carried less by their renown and thus can attract strong manuscripts even at a low rank. But to what extent does a venue imply a paper’s recognition and popularity? We propose a new approach and processing pipeline, in which we project a heterogeneous publication network, learn representations for papers and classify said papers according to their 2-year citation success. This framework provides a groundwork for a venue recommendation system, a tool for improving paper’s scientific recognition through a better choice of a publishing venue.

CLAVER: An integrated framework of convolutional layer, bidirectional LSTM with attention mechanism based scholarly venue recommendation

Scholarly venue recommendation is an emerging field due to a rapid surge in the number of scholarly venues concomitant with exponential growth in interdisciplinary research and cross collaboration among researchers. Finding appropriate publication venues is confronted as one of the most challenging aspects in paper publication as a larger proportion of manuscripts face rejection due to a disjunction between the scope of the venue and the field of research pursued by the research article. We present CLAVER--an integrated framework of Convolutional Layer, bi-directional LSTM with an Attention mechanism-based scholarly VEnue Recommender system. The system is the first of its kind to integrate multiple deep learning-based concepts, that requires only the abstract and the title of a manuscript to identify academic venues. An extensive and exhaustive set of experiments conducted on the DBLP dataset certify that the postulated model CLAVER performs better than most of the modern techniques as entrenched by standard metrics such as stability, accuracy, MRR, average venue quality, precision@k, nDCG@k and diversity.

Deep Learning Driven Venue Recommender for Event-Based Social Networks

Event-based online social platforms, such as Meetup and Plancast, have experienced increased popularity and rapid growth in recent years. In EBSN setup, selecting suitable venues for hosting events, which can attract a great turnout, is a key challenge. In this paper, we present a deep learning based venue recommendation system $DeepVenue$ D e e p V e n u e which provides context driven venue recommendations for the Meetup event-hosts to host their events. The crux of the proposed model relies on the notion of similarity between multiple Meetup entities such as events, venues, groups, etc. We develop deep learning techniques to compute a compact descriptor for each entity, such that two entities (say, venues) can be compared numerically. Notably, to mitigate the scarcity of venue related information in Meetup, we leverage on the cross domain knowledge transfer from popular LBSN service Yelp to extract rich venue related content. For hosting an event, the proposed $DeepVenue$ D e e p V e n u e model computes a success score for each candidate venue and ranks those venues according to the scores and finally recommend the top k venues. Our rigorous evaluation on the Meetup data collected for the city of Chicago shows that $DeepVenue$ D e e p V e n u e significantly outperforms the baselines algorithms. Precisely, for 84 percent of events, the correct hosting venue appears in the top 5 of the $DeepVenue$ D e e p V e n u e recommended list.

HASVRec: A modularized Hierarchical Attention-based Scholarly Venue Recommender system

Manually selecting appropriate scholarly venues is becoming a tedious and time-consuming task for researchers due to many reasons that include relevance, scientific impact, and research visibility. Sometimes, high-quality papers get rejected due to mismatch between the area of the paper and the scope of the journal. Recommending appropriate academic venues can, therefore, enable researchers to identify and take part in relevant conferences and publish in journals that matter the most. A researcher may certainly know of a few leading venues for her specific field of interest. However, a venue recommendation system becomes particularly helpful when exploring a new domain or when more options are needed. Due to high dimensionality and sparsity of text data, and complex semantics of the natural language, journal identification presents difficult challenges. We propose a novel and unified architecture that contains a Bi-directional LSTM (Bi-LSTM) and a Hierarchical Attention Network (HAN) to address the above problems. We call the proposed architecture modularized Hierarchical Attention-based Scholarly Venue Recommender system (HASVRec), which only requires the abstract, title, keywords, field of study, and author of a new paper along with its past publication record to recommend scholarly venues. Experiments on the DBLP-Citation-Network V11 dataset exhibit that our proposed approach outperforms several state-of-the-art methods in terms of accuracy, F1, nDCG, MRR, average venue quality, and stability.

Testing for Recommendation Method in M-Health Sports Venue Recommendation System

Exercising can make the body produce more endorphins so people with regular exercise are more resistant to stress than those who have little physical activity. We can get a recommendation of the sport venue in search engine, but it can’t accommodate personal preference. The mobile application for sports venue recommendations (M-health) with specific attribute weighting that can accommodate user preference for a specific attribute can be implemented with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm. TOPSIS was chosen as a recommendation algorithm because it has a relatively low level of algorithm complexity, so it is suitable to apply in mobile devices. To test the recommendations that are processed with TOPSIS, correlation testing is done as an alternative test besides accuracy. In general, the system takes the user's location coordinates and then recommends a Futsal court based on location, price and the number of courts. First, the user is inquired to enter the weights for each criterion. Then the user gets a recommendation for a Futsal court recommendation according to the user's current location. If the user wants detailed information about the desired futsal location, the user can click on one of the futsal places and then the detail page will be appear. After seeing the details of the selected futsal place, users can view the map to go to the relevant sports venue from the user's current location. Testing the recommendation system was based on correlation testing to see the correlation between the recommendations built by the system compared to the user's preference choices. Correlation testing was carried out to see whether there was a relationship between the results of the TOPSIS recommendation and the user's preference of sport venue. The correlation between them shows a positive correlation with a value of 0.770769231.

A scalable model for real‐time venue recommendations using MapReduce

SummaryThe popular social networks such as Facebook, Twitter, and Foursquare closely monitor user activities to recommend different services and events. Among others, venue recommendation proposes users the most appropriates venues based on the user preferences. It offers facility to the user to mark the check‐ins when a venue is visited. The traditional venue recommendation systems have opted collaborative filtering to propose recommendations. However, collaborative filtering overlooked certain critical issues, including real‐time recommendations, cold start, and scalability, for venue recommendations. Moreover, real‐time physical factors such as distance from the venue are also not considered in traditional venue recommendation systems. Furthermore, parsing and processing of huge volume of unstructured data is the main challenge for conventional recommender systems, particularly when dealing with real‐time recommendations. For efficient scaling, significant computational and storage resources for recommendation systems are desired. This article proposes a Real‐Time Venue Recommendation (RTVR) model that utilizes cloud‐based MapReduce framework to process, compare, mine, and manage large data sets for generating recommendations. The results showed that the proposed model has improved accuracy for real‐time recommendations. The proposed RTVR is more scalable as it exploits a cloud‐based architecture. Moreover, the proposed techniques are verified using formal verification methods.

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.

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.

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.

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.

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.

OmniSuggest: A Ubiquitous Cloud-Based Context-Aware Recommendation System for Mobile Social Networks

The evolution of mobile social networks and the availability of online check-in services, such as Foursquare and Gowalla, have initiated a new wave of research in the area of venue recommendation systems. Such systems recommend places to users closely related to their preferences. Although venue recommendation systems have been studied in recent literature, the existing approaches, mostly based on collaborative filtering, suffer from various issues, such as: 1) data sparseness, 2) cold start, and 3) scalability. Moreover, many existing schemes are limited in functionality, as the generated recommendations do not consider group of “friends” type situations. Furthermore, the traditional systems do not take into account the effect of real-time physical factors (e.g., distance from venue, traffic, and weather conditions) on recommendations. To address the aforementioned issues, this paper proposes a novel cloud-based recommendation framework OmniSuggest that utilizes: 1) Ant colony algorithms, 2) social filtering, and 3) hub and authority scores, to generate optimal venue recommendations. Unlike existing work, our approach suggests venues at a finer granularity for an individual or a “group” of friends with similar interest. Comprehensive experiments are conducted with a large-scale real dataset collected from Foursquare. The results confirm that our method offers more effective recommendations than many state of the art schemes.