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Consumer reviews play an important role in various e-commerce sites like hotel reservation and app stores. Online consumer reviews are informative because they convey consumers' actual experiences and evaluations to the products and services they received. In this paper, we leverage the consumer reviews to develop a review-driven recommender service for e-commerce websites. We semantically explore...
Detecting security threats from compromised account or malicious insider by leveraging enterprise traffic logs is the goal of user behavior-based analytics. For its ease of interpretation, a common analytic indicator used in the industry for user behavior analytics is whether a user accesses a network entity, such as a machine or process, for the first time. While this popular indicator does correlate...
Recommender systems are software tools and techniques which aim at suggesting to users items they might be interested in. Context-aware recommender systems are a particular category of recommender systems which exploit contextual information to provide more adequate recommendations. However, recommendation engines still suffer from the cold-start problem, namely where not enough information about...
Crowdsourcing systems are distributed problem solving platforms, where small tasks are channelled to a crowd in the form of open calls for solutions. Reward based crowdsourcing systems tries to attract the interested and capable workers to provide solutions in return for monetary rewards. We study the task recommendation problem in reward based crowdsourcing platforms, where we leverage both implicit...
Tourism recommender system relies on several items in supporting its effectiveness in the context. The items' searching and selecting needed tools, such recommender system. The item concerns with the contextual information, such as location, time, or social. Recommendations that use contextual information in processing recommendations are Context-Aware Recommender System. However, to identify and...
In case that individuals feel their privacy is violated while using any recommender system, they might be willing to declare incorrect information or even completely refuse to use such services. To relieve customer concerns, privacy risks that are inherent in the utilization of such systems need to be discussed principally, and service providers should offer privacy-preservation mechanisms. Also,...
Offering personalised recommendations to visitors of a museum is a complex problem inherent to physical spaces. When at the same time specific applicative or museum objectives have to be taken into account, this becomes even more complicated. We introduce here a graph-based semantic recommender approach relying on ontological formalisation of knowledge about manipulated entities to solve the multi-dimensional...
In this paper, we introduce a pruning algorithm which removes aged user ratings from the rating database used by collaborative filtering algorithms, in order to (1) improve prediction quality and (2) minimize the rating database size, as well as the rating prediction generation time. The proposed algorithm needs no extra information concerning the items' characteristics (e.g. categories that they...
Personalization of movie recommendations is a widely researched topic. Personalization is usually carried out using local resources that are available at one's disposal. This local resource presents a snapshot of user preference at a particular moment. It doesn't address the long term user preferences. These concerns can be addressed using resources available with the user. This paper proposes a model...
In event-based social networks, an event recommender helps users to discover events that align with their preferences from a large number of upcoming events. In this paper, we propose a personalized event recommender called SoCaST based on the geographical, categorical, social and temporal influences of events on users to provide event recommendations. SoCaST uses an adaptive Kernel Density Estimation...
The key to a recommendation system is the prediction of users' preferences. Personalized recommendation for many online music applications depends on the prediction of both long-term as well as the short-term preferences. In this paper, we propose a novel personalized next-song recommendation system that jointly consider the long-term and short-term preferences in its design. To depict the long-term...
In recent years, there has been a tremendous increase in the popularity of event-based social networks which allow social and physical interactions among their members. One major challenge for their members is the difficulty of searching events that meet their preferences from a large number of upcoming events. To tackle this challenge, we propose a personalized event recommendation framework called...
The goal of our study is to provide a holistic view on various ethical challenges that complicate the design and use of recommender systems (RS). Our findings materialize into an ethical recommendation framework, which maps RS development stages to the corresponding ethical concerns, and further down to known solutions and the proposed user-adjustable controls. The need for such a framework is dictated...
Social networking websites allows us to understand the user's interest and behavior pattern on various Travel and Tourism services, especially travel attractions and point of interest, which can be exploited to recommend personalized list of places to users. The major challenge faced by Travel and Tourism recommendation System is to understand the implicit relationships that exist between the user...
With the advent of e-commerce, there are hundreds of websites being deployed and each site offers millions of products. This means that a substantial amount of information is being provided by these websites which cause the problem of information overload and in turn results in reduced customer satisfaction and interest. Recommender systems are designed to overcome such problems. They are intelligent...
Nowadays recommender systems is a software that is used as an important tool of e-commerce, which helps to analyze users' tastes and provide them with lists of products that they would like to prefer. This paper is an investigation of using collaborative filtering techniques for a music recommender system. Collaborative filtering is the technology that focuses on the relationships between users and...
Recent recommender systems work well in terms of prediction accuracy, making use of a variety of features, such as users' personal information, purchasing history, browsing history and comments. However, traditional recommendation models have not made full use of item information and met difficulties with cold-start problems. On the other hands, visual information on item images is one of the most...
This paper proposes CPERS, a contextual and personalized event recommender system that exploits overall user preference and context influences to produce recommendations in event-based social networks (EBSNs). Diversely from items in traditional recommendation scenarios (e.g. movies, songs), events in EBSNs are only valid for a short period of time, having no explicit feedback. Therefore the event...
Location-based social networks (LBSNs) make it possible for servers to record users' location histories, mine their life patterns, and infer individual preferences. As an important component of LBSNs, recommender systems gained popularity in recent years. Recommender systems can automatically list candidate locations for users according to their preferences, which is different from traditional search...
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