Recommender systems (RSs) provide the personalized recommendations to users for specific items in a wide range of applications such as e-commerce, media recommendations and social networking applications. Collaborative Filtering (CF) and Content Based (CB) Filtering are two methods which have been employed in implementing the recommender systems. CF suffers from Cold Start (CS) problem where no rating records (Complete Cold Start CSS) or very few records (Incomplete Cold Start ICS) are available for newly coming users and items. The performance of CB methods relies on good feature extraction methods so that the item descriptions can be used to measure items similarity as well as for user profiling. This paper addresses the CS problem by providing a novel way of integrating content embeddings in CF. The proposed algorithm (HRS-CE) generates the user profiles that depict the type of content in which a particular user is interested. The word embedding model (Word2Vec) is used to produce distributed representation of items descriptions. The higher representation for an item description, obtained using content embeddings, are combined with similarity techniques to perform rating predictions. The proposed method is evaluated on two public benchmark datasets (MovieLens 100k and MovieLens 20M). The results demonstrate that the proposed model outperforms the state of the art recommender system models for CS items.
Indirect trust computation based on recommendations form an important component in trust-based access control models for pervasive environment. It can provide the service provider the confidence to interact with unknown service requesters. However, recommendation-based indirect trust computation is vulnerable to various types of attacks. This paper proposes a defense mechanism for filtering out dishonest recommendations based on a measure of dissimilarity function between the two subsets. A subset of recommendations with the highest measure of dissimilarity is considered as a set of dishonest recommendations. To analyze the effectiveness of the proposed approach, we have simulated three inherent attack scenarios for recommendation models (bad mouthing, ballot stuffing, and random opinion attack). The simulation results show that the proposed approach can effectively filter out the dishonest recommendations based on the majority rule. A comparison between the exiting schemes and our proposed approach is also given.
The inevitable revolution of the Internet of Things (IoT) and its benefits can be witnessed everywhere. Two major issues related to IoT are the interoperability and the identification of trustworthy things. The proposed Context-Aware Trustworthy Social Web of Things System (CATSWoTS) addresses the interoperability issue by incorporating web technologies including Service Oriented Architecture where each thing plays the role of a service provider as well as a role of service consumer. The aspect of social web helps in getting recommendations from social relations. It was identified that the context dependency of trust along with Quality of Service (QoS) criteria, for identifying and recommending trustworthy Web of Things (WoT), require more attention. For this purpose, the parameters of context awareness and the constraints of QoS are considered. The research focuses on the idea of a user-centric system where the profiles of each thing (level of trustworthiness) are being maintained at a centralized level and at a distributed level as well. The CATSWoTS evaluates service providers based on the mentioned parameters and the constraints and then identifies a suitable service provider. For this, a rule-based collaborative filtering approach is used. The efficacy of CATSWoTS is evaluated with a specifically designed environment using a real QoS data set. The results showed that the proposed novel technique fills the gap present in the state of the art. It performed well by dynamically identifying and recommending trustworthy services as per the requirements of a service seeker.
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