5,720 research outputs found
Probabilistic Personalized Recommendation Models For Heterogeneous Social Data
Content recommendation has risen to a new dimension with the advent of platforms like Twitter, Facebook, FriendFeed, Dailybooth, and Instagram. Although this uproar of data has provided us with a goldmine of real-world information, the problem of information overload has become a major barrier in developing predictive models. Therefore, the objective of this The- sis is to propose various recommendation, prediction and information retrieval models that are capable of leveraging such vast heterogeneous content. More specifically, this Thesis focuses on proposing models based on probabilistic generative frameworks for the following tasks: (a) recommending backers and projects in Kickstarter crowdfunding domain and (b) point of interest recommendation in Foursquare. Through comprehensive set of experiments over a variety of datasets, we show that our models are capable of providing practically useful results for recommendation and information retrieval tasks
RELINE: Point-of-Interest Recommendations using Multiple Network Embeddings
The rapid growth of users' involvement in Location-Based Social Networks
(LBSNs) has led to the expeditious growth of the data on a global scale. The
need of accessing and retrieving relevant information close to users'
preferences is an open problem which continuously raises new challenges for
recommendation systems. The exploitation of Points-of-Interest (POIs)
recommendation by existing models is inadequate due to the sparsity and the
cold start problems. To overcome these problems many models were proposed in
the literature, but most of them ignore important factors such as: geographical
proximity, social influence, or temporal and preference dynamics, which tackle
their accuracy while personalize their recommendations. In this work, we
investigate these problems and present a unified model that jointly learns
users and POI dynamics. Our proposal is termed RELINE (REcommendations with
muLtIple Network Embeddings). More specifically, RELINE captures: i) the
social, ii) the geographical, iii) the temporal influence, and iv) the users'
preference dynamics, by embedding eight relational graphs into one shared
latent space. We have evaluated our approach against state-of-the-art methods
with three large real-world datasets in terms of accuracy. Additionally, we
have examined the effectiveness of our approach against the cold-start problem.
Performance evaluation results demonstrate that significant performance
improvement is achieved in comparison to existing state-of-the-art methods
A new technique for intelligent web personal recommendation
Personal recommendation systems nowadays are very important in web applications
because of the available huge volume of information on the World Wide Web, and the
necessity to save users’ time, and provide appropriate desired information, knowledge,
items, etc. The most popular recommendation systems are collaborative filtering systems,
which suffer from certain problems such as cold-start, privacy, user identification, and
scalability. In this thesis, we suggest a new method to solve the cold start problem taking
into consideration the privacy issue. The method is shown to perform very well in
comparison with alternative methods, while having better properties regarding user privacy.
The cold start problem covers the situation when recommendation systems have not
sufficient information about a new user’s preferences (the user cold start problem), as well
as the case of newly added items to the system (the item cold start problem), in which case
the system will not be able to provide recommendations. Some systems use users’
demographical data as a basis for generating recommendations in such cases (e.g. the
Triadic Aspect method), but this solves only the user cold start problem and enforces user’s
privacy. Some systems use users’ ’stereotypes’ to generate recommendations, but
stereotypes often do not reflect the actual preferences of individual users. While some other
systems use user’s ’filterbots’ by injecting pseudo users or bots into the system and consider
these as existing ones, but this leads to poor accuracy.
We propose the active node method, that uses previous and recent users’ browsing targets
and browsing patterns to infer preferences and generate recommendations (node
recommendations, in which a single suggestion is given, and batch recommendations, in
which a set of possible target nodes are shown to the user at once). We compare the active
node method with three alternative methods (Triadic Aspect Method, Naïve Filterbots
Method, and MediaScout Stereotype Method), and we used a dataset collected from online
web news to generate recommendations based on our method and based on the three
alternative methods. We calculated the levels of novelty, coverage, and precision in these
experiments, and we found that our method achieves higher levels of novelty in batch
recommendation while achieving higher levels of coverage and precision in node
recommendations comparing to these alternative methods. Further, we develop a variant of
the active node method that incorporates semantic structure elements. A further
experimental evaluation with real data and users showed that semantic node
recommendation with the active node method achieved higher levels of novelty than nonsemantic
node recommendation, and semantic-batch recommendation achieved higher levels
of coverage and precision than non-semantic batch recommendation
Applying reranking strategies to route recommendation using sequence-aware evaluation
Venue recommendation approaches have become particularly useful nowadays due to the increasing number of users registered in location-based social networks (LBSNs), applications where it is possible to share the venues someone has visited and establish connections with other users in the system. Besides, the venue recommendation problem has certain characteristics that differ from traditional recommendation, and it can also benefit from other contextual aspects to not only recommend independent venues, but complete routes or venue sequences of related locations. Hence, in this paper, we investigate the problem of route recommendation under the perspective of generating a sequence of meaningful locations for the users, by analyzing both their personal interests and the intrinsic relationships between the venues. We divide this problem into three stages, proposing general solutions to each case: First, we state a general methodology to derive user routes from LBSNs datasets that can be applied in as many scenarios as possible; second, we define a reranking framework that generate sequences of items from recommendation lists using different techniques; and third, we propose an evaluation metric that captures both accuracy and sequentiality at the same time. We report our experiments on several LBSNs datasets and by means of different recommendation quality metrics and algorithms. As a result, we have found that classical recommender systems are comparable to specifically tailored algorithms for this task, although exploiting the temporal dimension, in general, helps on improving the performance of these techniques; additionally, the proposed reranking strategies show promising results in terms of finding a trade-off between relevance, sequentiality, and distance, essential dimensions in both venue and route recommendation tasksThis work has been funded by the Ministerio de Ciencia, Innovación y Universidades (reference: TIN2016-80630-P) and by the European Social Fund (ESF), within the 2017 call for predoctoral contract
A Safety Support System for Children\u27s Antiloss
In the recent past, crimes against children and the number of the missing children have been stayed at high. It is a tragic disaster for a family if their child is missing. Feeling safe about their children is very important for the parents. Therefore, there is an urgent requirement for safety support systems to prevent crimes against children and for anti-loss, particularly when the children are on their own, such as on the ways to and from schools. Thanks to the highly development of telecommunication and mobile technologies, preventive devices such as child ID kits, family trackers have come to light. However, they haven\u27t been impressive solutions yet as they only track current positions of the children and lack of intimations for the parents when their children are under potential dangers. In this thesis, a data mining framework is introduced, in which secure areas and secure paths of the children are learned based on their location histories. When the system predicts the children to be potentially unsafe (e.g., in a strange area or on a strange route), automatic reports will be sent to their parents. Furthermore, an indoor positioning method utilizing Bluetooth is also proposed. Based on the android platform, a prototype of the application for both children and parents is developed incorporating with the proposed techniques in this thesis
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