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Ads and the City: Considering Geographic Distance Goes a Long Way
"... Social-networking sites have started to offer tools that suggest “guests ” who should be invited to user-defined social events (e.g., birthday parties, networking events). The problem of how to recommend people to events is similar to the more traditional (recommender system) problem of how to recom ..."
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Social-networking sites have started to offer tools that suggest “guests ” who should be invited to user-defined social events (e.g., birthday parties, networking events). The problem of how to recommend people to events is similar to the more traditional (recommender system) problem of how to recommend events (items) to people (users). Yet, upon Foursquare data of “who visits what ” in the city of London, we show that a state-of-the-art recommender system does not perform well- mainly because of data sparsity. To fix this problem, we add domain knowledge to the recommendation process. From the complex system literature in human mobility, we learn two insights: 1) there are special individuals (often called power users) who visit many places; and 2) individuals go to a venue not only because they like it but also because they are closeby. We model these insights into two simple models and learn that: 1) simply recommending power users works better than random but is far from producing the best recommendations; 2) an item-based recommender system produces accurate recommendations; and 3) recommending places that are closest to a user’s geographic center of interest produces recommendations that are as accurate as, if not more accurate than, item-based recommender’s. This last result has practical implications as it offers guidelines for designing locationbased recommender systems and for partly addressing cold-start situations.
Mining future spatiotemporal events and their sentiment from online news articles for location-aware recommendation system
- In Proceedings 1st ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems (MobiGIS
, 2012
"... The future-related information mining task for online web resources such as news articles and blogs has been getting more attention due to its potential usefulness in supporting individual’s decision mak-ing in a world where massive new data are generated daily. Instead of building a data-driven mod ..."
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The future-related information mining task for online web resources such as news articles and blogs has been getting more attention due to its potential usefulness in supporting individual’s decision mak-ing in a world where massive new data are generated daily. Instead of building a data-driven model to predict the future, one extracts future events from these massive data with high probability that they occur at a future time and a specific geographic location. Such spatiotemporal future events can be utilized by a recommender sys-tem on a location-aware device to provide localized future event suggestions. In this paper, we describe a systematic approach for mining fu-ture spatiotemporal events from web; in particular, news articles. In our application context, a valid event is defined both spatially and temporally. The mining procedure consists of two main steps:
The call of the crowd: Event participation in location-based social services
- In Proceedings of the 8th International AAAI Conference on Weblogs and Social
, 2014
"... Understanding the social and behavioral forces behind event participation is not only interesting from the view-point of social science, but also has important applica-tions in the design of personalized event recommender systems. This paper takes advantage of data from a widely used location-based ..."
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Understanding the social and behavioral forces behind event participation is not only interesting from the view-point of social science, but also has important applica-tions in the design of personalized event recommender systems. This paper takes advantage of data from a widely used location-based social network, Foursquare, to analyze event patterns in three metropolitan cities. We put forward several hypotheses on the motivating factors of user participation and confirm that social as-pects play a major role in determining the likelihood of a user to participate in an event. While an explicit social filtering signal accounting for whether friends are at-tending dominates the factors, the popularity of an event proves to also be a strong attractor. Further, we capture an implicit social signal by performing random walks in a high dimensional graph that encodes the place type preferences of friends and that proves especially suited to identify relevant niche events for users. Our findings on the extent to which the various temporal, spatial and social aspects underlie users ’ event preferences lead us to further hypothesize that a combination of factors bet-ter models users ’ event interests. We verify this through a supervised learning framework. We show that for one in three users in London and one in five users in New York and Chicago it identifies the exact event the user would attend among the pool of suggestions.
Social Recommendations for Events
"... Due to an abundance of available events, selecting the most interesting events and deciding who to invite to attend these events becomes increasingly difficult. This paper describes the Outlife recommender, which assists in finding the ideal event by providing recommendations based on the user’s per ..."
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Due to an abundance of available events, selecting the most interesting events and deciding who to invite to attend these events becomes increasingly difficult. This paper describes the Outlife recommender, which assists in finding the ideal event by providing recommendations based on the user’s personal preferences. For each event recommendation, the Outlife recommender suggests a group of friends to invite to the event. Conversely, the user can select a group of friends and receive group recommendations based on the preferences of all group members. Compared to traditional group recommenders, which assume the group composition is available as input, the Outlife recommender automatically composes the user’s groups of best friends based on interaction behavior. A user evaluation showed that the composition of groups of friends is accurate and that the offered recommendations match the user’s preferences.
Event Recommendation in Event-based Social Networks
"... With the large number of events published all the time in event-based social networks (EBSN), it has become increas-ingly difficult for users to find the events that best match their preferences. Recommender systems appear as a natu-ral solution to this problem. However, the event recommen-dation sc ..."
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With the large number of events published all the time in event-based social networks (EBSN), it has become increas-ingly difficult for users to find the events that best match their preferences. Recommender systems appear as a natu-ral solution to this problem. However, the event recommen-dation scenario is quite different from typical recommenda-tion domains (e.g. movies), since there is an intrinsic new item problem involved (i.e. events can not be ”consumed” before their occurrence) and scarce collaborative informa-tion. Although some few works have appeared in this area, there is still lacking in the literature an extensive analysis of the different characteristics of EBSN data that can af-fect the design of event recommenders. In this paper we provide a contribution in this direction, where we investi-gate and discuss important features of EBSN such as spar-sity, events life time, co-participation of users in events and geographic features. We also shed some light on the per-formance and limitations of several well known recommen-dation algorithms and combinations of them on real data collected from meetup.com.
Hybrid event recommendation using . . .
, 2013
"... An ever increasing number of social services offer thousands of diverse events per day. Users tend to be overwhelmed by the massive amount of information available, especially with limited browsing options perceived in many event web services. To alleviate this information overload, a recommender sy ..."
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An ever increasing number of social services offer thousands of diverse events per day. Users tend to be overwhelmed by the massive amount of information available, especially with limited browsing options perceived in many event web services. To alleviate this information overload, a recommender system becomes a vital component for assisting users selecting relevant events. However, such system faces a number of challenges owed to the the inherent complex nature of an event. In this paper, we propose a novel hybrid approach built on top of Semantic Web. On the one hand, we use a content-based system enriched with Linked Data to over-come the data sparsity, a problem induced by the transiency of events. On the other hand, we incorporate a collaborative filtering to involve the social aspect, an influential feature in decision making. This hybrid system is enhanced by the integration of a user diversity model designed to detect user propensity towards specific topics. We show how the hybridization of CB+CF systems and the integration of interest diversity features are important to improve predictions. Experimental results demonstrate the effectiveness of our approach using precision and recall measures.
Spontaneous Event Recommendations on the Go
"... Abstract. In this paper, we summarize our previous work in the field of event recommendations and give an outlook on future work. We developed a recommender system which implements a hybrid recommendation technique to provide accurate recommendations. A two-week user study showed that our system de ..."
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Abstract. In this paper, we summarize our previous work in the field of event recommendations and give an outlook on future work. We developed a recommender system which implements a hybrid recommendation technique to provide accurate recommendations. A two-week user study showed that our system delivers promising results. Nevertheless, we believe that this approach mainly supports users who are looking for recommendations a long time in advance. Other possible situations in which people could be open for recommendations are more spontaneous, for example, when they are already out exploring the city. We give an overview of aspects which have to be considered fur such spontaneous recommendations on the go and the role of social context in this scenario.
Context-Aware Event Recommendation in Event-based Social Networks
"... The Web has grown into one of the most important channels to communicate social events nowadays. However, the sheer volume of events available in event-based social networks (EBSNs) often undermines the users ’ ability to choose the events that best fit their interests. Recommender systems appear as ..."
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The Web has grown into one of the most important channels to communicate social events nowadays. However, the sheer volume of events available in event-based social networks (EBSNs) often undermines the users ’ ability to choose the events that best fit their interests. Recommender systems appear as a natural solution for this problem, but differ-ently from classic recommendation scenarios (e.g. movies, books), the event recommendation problem is intrinsically cold-start. Indeed, events published in EBSNs are typically short-lived and, by definition, are always in the future, hav-ing little or no trace of historical attendance. To overcome this limitation, we propose to exploit several contextual sig-nals available from EBSNs. In particular, besides content-based signals based on the events ’ description and collabo-rative signals derived from users ’ RSVPs, we exploit social signals based on group memberships, location signals based on the users ’ geographical preferences, and temporal sig-nals derived from the users ’ time preferences. Moreover, we combine the proposed signals for learning to rank events for personalized recommendation. Thorough experiments using a large crawl of Meetup.com demonstrate the effectiveness of our proposed contextual learning approach in contrast to state-of-the-art event recommenders from the literature.
Exploring the Impact of Dynamic Mutual Influence on Social Event Participation ∗
"... Nowadays, it is commonly seen that an offline social event is organized through online social network services (SNS), in this way cyber strangers can be connected in physical world. While there are some preliminary studies on social event participation through SNS, they usually have more focus on th ..."
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Nowadays, it is commonly seen that an offline social event is organized through online social network services (SNS), in this way cyber strangers can be connected in physical world. While there are some preliminary studies on social event participation through SNS, they usually have more focus on the mining of event profiles and have less focus on the so-cial relationships among target users. In particular, the im-portance of dynamic mutual influence among potential event participants has been largely ignored. In this paper, we develop a novel discriminant framework, which allows to integrate the dynamic mutual dependence of potential event participants into the discrimination process. Specifically, we formulate the group-oriented event participation problem as a variant two-stage discriminant framework to capture the users ’ preferences as well as their latent social connections. The experimental results on real-world data show that our method can effectively predict the event participation with a significant margin compared with several state-of-the-art baselines, which validates the hypothesis that dynamic mu-tual influence could play an important role in the decision-making process of social event participation. 1
doi:10.1093/iwcomp/xxxxxx Should I Stay or Should I Go? Improving Event Recommendation in the Social Web
"... This paper focuses on the recommendation of events in the Social Web, and addresses the problem of finding if, and to which extent, certain features, which are peculiar to events, are relevant in predicting the users ’ interests and should thereby be taken into account in recommendation. We consider ..."
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This paper focuses on the recommendation of events in the Social Web, and addresses the problem of finding if, and to which extent, certain features, which are peculiar to events, are relevant in predicting the users ’ interests and should thereby be taken into account in recommendation. We consider in particular three “additional ” features that are usually shown to users within social networking environments: reachability from the user location, the reputation of the event in the community, and the participation of the user’s friends. Our study is aimed at evaluating whether adding this information to the description of the event type and topic, and including in the user profile the information on the relevance of these factors, can improve our capability to predict the user’s interest. We approached the problem by carrying out two surveys with users, who were asked to express their interest in a number of events. We then trained, by means of linear regression, a scoring function defined as a linear combination of the different factors, whose goal was to predict the user scores. We repeated this experiment under different hypotheses on the additional factors, in order to assess their relevance by comparing the predictive capabilities of the resulting functions. The compared results of our experiments show that additional factors, if properly weighted, can improve the prediction accuracy with an error reduction of 4.1%. The best results were obtained by combining content-based factors and additional factors in a proportion of approximately 10: 4. Categories and subject descriptors: recommender systems; contextual factors; user studies
