1,721,084 research outputs found
Group recommender systems: State of the art, emerging aspects and techniques, and research challenges
No full textA recommender system aims at suggesting to users items that might interest them and that they have not considered yet. A class of systems, known as group recommendation, provides suggestions in contexts in which more than one person is involved in the recommendation process. The goal of this tutorial is to provide the ECIR audience with an overview on group recommendation. We will first illustrate the recommender systems principles, then formally introduce the problem of producing recommendations to groups, and present a survey based on the tasks performed by these systems. We will also analyze challenging topics like their evaluation, and present emerging aspects and techniques in this area. The tutorial will end with a summary that highlights open issues and research challenges
Group recommender systems
No full textGroup recommender systems provide suggestions in contexts in which people operate in groups. The goal of this tutorial is to provide the RecSys audience with an overview on group recommendation. We will first formally introduce the prob- lem of producing recommendations to groups, then present a survey based on the tasks performed by these systems. We will also analyze challenging topics like their evaluation, and present emerging aspects and techniques in this area. The tutorial will end with a summary that highlights open issues and research challenges
Impact of content novelty on the accuracy of a group recommender system
No full textA group recommender system is designed for contexts in which more than a person is involved in the recommendation process. There are types of content (like movies) for which it would be advisable to recommend an item only if it has not yet been consumed by most of the group. In fact, it would be trivial and not significant to recommended an item if a great part of the group has already expressed a preference for it. This paper studies the impact of content novelty on the accuracy of a group recommender system, by introducing a constraint on the percentage of a group for which the recommended content has to be novel. A comparative analysis in terms of different values of the percentage of the group and for groups of different sizes, was validated through statistical tests, in order to evaluate when the difference in the accuracy values is significant. Experimental results, deeply analyzed and discussed, show that the recommendation of novel content significantly affects the performances only for small groups and only when content has to be novel for the majority of it
The rating prediction task in a group recommender system that automatically detects groups: architectures, algorithms, and performance evaluation
No full textA recommender system suggests items to users by predicting what might be interesting for them. The prediction task has been highlighted in the literature as the most important one computed by a recommender system. Its role becomes even more central when a system works with groups, since the predictions might be built for each user or for the whole group. This paper presents a deep evaluation of three approaches, used for the prediction of the ratings in a group recommendation scenario in which groups are detected by clustering the users. Experimental results confirm that the approach to predict the ratings strongly influences the performance of a system and show that building predictions for each user, with respect to building predictions for a group, leads to great improvements in the accuracy of the recommendations. © 2014 Springer Science+Business Media New Yor
Using Collaborative Filtering to Overcome the Curse of Dimensionality when Clustering Users in a Group Recommender System
No full textExploring the Ratings Prediction Task in a Group Recommender System that Automatically Detects Groups
No full textRecommender systems produce content for users, by suggesting items that users might like. Predicting the ratings is a key task in a recommender system. This is especially true in a system that works with groups, because ratings might be predicted for each user or for the groups. The approach chosen to predict the ratings changes the architecture of the system and what information is used to build the predictions. This paper studies approaches to predict the ratings in a group recommendation scenario that automatically detects groups. Experimental results confirm that the approach to predict the ratings strongly influences the performances of a system and show that building predictions for each user, with respect to building predictions for a group, leads to great improvements in the quality of the recommendations
ART: group recommendation approaches for automatically detected groups
Full text linkGroup recommender systems provide suggestions when more than a person is involved in the recommendation process. A particular context in which group recommendation is useful is when the number of recommendation lists that can be generated is limited (i.e., it is not possible to suggest a list of items to each user). In such a case, grouping users and producing recommendations to groups becomes necessary. None of the approaches in the literature is able to automatically group the users in order to overcome the previously presented limitation. This paper presents a set of group recommender systems that automatically detect groups of users by clustering them, in order to respect a constraint on the maximum number of recommendation lists that can be produced. The proposed systems have been largely evaluated on two real-world datasets and compared with hundreds of experiments and statistical tests, in order to validate the results. Moreover, we introduce a set of best practices that help in the development of group recommender systems in this context
A proactive Time-Frame Convolution Vector (TFCV) technique to detect frauds attempts in e-commerce transactions
No full textAny business that operates on the Internet and accepts payments through debit or credit cards, also implicitly accepts that some transaction may be fraudulent. The design of effective strategies to face this problem is challenging, due to factors such as the heterogeneity and the non stationary distribution of the data, as well as the presence of an imbalanced class distribution, and the scarcity of public datasets. Differently from the state-of-the-art strategies, instead of producing a unique model based on the past transactions of the users, our approach generates a set of models (behavioral patterns) to evaluate a new transaction, by considering the behavior of the user in different temporal frames of her/his history. By using only the legitimate past transactions of a user, we can operate in a proactive manner, by detecting the fraudulent ones that have never occurred. This also overcomes the data imbalance that afflicts the state-of-the-art approaches. We evaluate our proposal by comparing it with one of the most performing approaches at the state of the art (i.e., Random Forests), using a real-world credit card dataset
Investigating the role of the rating prediction task in granularity-based group recommender systems and big data scenarios
No full textNowadays, one important issue for companies is the efficient dealing of the big data prob- lem , which means that their business intelligence has to manage huge amounts of data. An interesting case in point is flyers distribution. Research and market figures prove that the distribution of advertising flyers still represents a valuable tool to attract potential customers to a company. It goes without saying that including personalized content in a company’s flyer is more likely to yield better results than offering the same flyer to all potential clients. However, producing personalized flyers would imply unaffordable costs for a company. An efficient trade-offsolution between accuracy and costs could be to de- fine a maximum number of different flyers addressing different groups of users interested in their content. In order to systematically support this and similar trade-offsolutions, we propose a novel type of group recommendations, which is able to detect a number of groups of end-users equal to the number of recommendation lists (e.g., flyers) that can be produced (i.e., the granularity with which the system can operate). Moreover, it can pro- vide suggestions to the detected specific groups of users. In particular, we focus on the rating prediction for those items users do not evaluate. Indeed, rating prediction represents the main task that a recommender system is asked to perform and it becomes even more central if included into a group recommender system, since the predictions might be built for each user or for each group. Our approach also gives the possibility to efficiently man- age the curse of the dimensionality phenomena caused by the sparsity of the ratings arising from big data handling. We present four granularity-based group recommender systems using different rating prediction algorithms and architectures. These systems employ the same algorithms to carry out other tasks (i.e., those that do not predict the ratings) and this allows us to evaluate which rating prediction approach is the most effective in terms of accuracy. Experiments on two real-world datasets show that, unlike group predictions, single user predictions can lead to improvements in the recommendation accuracy and the dealing of the curse of the dimensionality phenomena
Towards chatbots as recommendation interfaces
No full textProviding tourists with the information they need in a timely and understandable manner is a key objective for promoting cultural heritage. Currently, tourists often spend only a few days in a city or in an entire region, so they need recommendations for optimizing the visit. In this paper, we introduce the objectives of the Tourisitific project, in which we aim to create a recommender system for travel information, supporting a lightweight access through chatbots. In this way, we will support travellers during their visits without burdening them with the installation of dedicated apps. In addition, we will help small and micro touristic operators in building their presence on the web with a familiar and easy to manage interface
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