A Group Discovery Method Based on Collaborative Filtering and Knowledge Graph for IoT Scenarios

Abstract : With the massive growth of Internet-of-Things (IoT) devices, how to provide users with recommendation services in the IoT environment has become a research hotspot. Group discovery, as a prerequisite step of group recommendation that can be used to assist groups of users to select services in IoT-enriched environments, has an important impact on recommendation performance. However, existing group recommendation solutions assume that a user belongs to a specific group and ignore the possible correlation between the user's preferences and other groups' preferences. In addition, existing solutions treat group members as equal individuals and assign them equal weights, which makes it hard to meet the user's accurate recommendation requirements. Furthermore, these methods focus on group members' explicit preference information while ignoring implicit preferences. To address these problems, we propose a group discovery method based on collaborative filtering and knowledge graph (GD-CFKG). This method first uses the attention mechanism to learn the embedding of service entities from knowledge graphs and interaction between users and services to achieve users' own preferences embedding. Considering that the preferences of similar users will help to attain accurate target user's preferences, we then train users' final preferences embedding by collaborative filtering and word2vec method. We conduct experiments to evaluate our approach using the MovieLens and Douban data sets. Experimental results show that our proposed method has better group recommendation performance than those baseline methods.
 EXISTING SYSTEM :
 ? They enable developers to assess the recommender’s ability to provide ratings for energy suggestions that are consistent with existing users’ ratings, thus, evaluating the quality of the given suggestions. ? While for the objectives of the recommender systems, almost the same attention has been given for developing both action recommendations and strategy recommendations in existing energy saving recommendation systems frameworks. ? In this regard, datasets are utilized as benchmarks for developing new recommender models and comparing them to existing systems under the same conditions. ? Moreover, the lack of open-access repositories containing existing datasets make the recommender systems comparison very difficult, or even impossible.
 DISADVANTAGE :
 ? Collaborative filtering suffers mainly from two problems: sparsity of users’ data when there are few interactions between the user and the items, and cold-start problem (new user and new item). ? Content-based filtering (CBF) suffers from some limitations such as overspecialization, limited content analysis, serendipity, and new user problems. ? Knowledge graphs can provide complementary information to overcome the problems faced by collaborative and content-based filtering approaches, since their recommendations are not linked to ratings; instead, they use domain knowledge. ? The underlying advantage of doing this is to overcome the problem of data sparsity.
 PROPOSED SYSTEM :
 • Due to the high demand of personalization in most of real-life scenarios, various approaches of adopting recommender systems have been proposed based on the type of decision making the system has to support and the goal these recommendations have to meet. • A pairwise association rule-based algorithm is proposed for drawing the collective preferences of groups of end-users. • Several techniques have been proposed in the past, ranging from k-anonymity, to differential privacy and homomorphic encryption. • In the same manner, a fog-based recommender system which helps to bridge the gap between the cloud and end-devices is proposed.
 ADVANTAGE :
 ? To improve recommendation performance, usage, and scalability, the research has evolved by producing several generations of recommender systems. ? The goal of combination is to leverage each approach’s advantages and to improve the overall system performance . ? These measures are commonly used to evaluate the performance of recommender systems. ? Concerning the performance of the analyzed works, in general, an improvement is observed compared to other recommendation methods taken as a baseline. ? Most recommendation models focus on achieving good performance from the point of view of accuracy. However, if the user cannot interpret the results, then the reliability of the system is reduced.

We have more than 145000 Documents , PPT and Research Papers

Have a question ?

Mail us : info@nibode.com