Offline optimization for user-specific hybrid recommender systems

Massive availability of multimedia content has given rise to numerous recommendation algorithms that tackle the associated information overload problem. Because of their growing popularity, selecting the best one is becoming an overload problem in itself. Hybrid algorithms, combining multiple individual algorithms, offer a solution, but often require manual configuration and power only a few individual recommendation algorithms. In this work, we regard the problem of configuring hybrid recommenders as an optimization problem that can be trained in an offline context. Focusing on the switching and weighted hybridization techniques, we compare and evaluate the resulting performance boosts for hybrid configurations of up to 10 individual algorithms. Results showed significant improvement and robustness for the weighted hybridization strategy which seems promising for future self-adapting, user-specific hybrid recommender systems

On hybrid modular recommendation systems for video streaming

The recommendation systems aim to improve the user engagement by recommending appropriate personalized content to users, exploiting information about their preferences. We propose the enabler, a hybrid recommendation system which employs various machine-learning (ML) algorithms for learning an efficient combination of several recommendation algorithms and selects the best blending for a given input.Specifically, it integrates three layers, namely, the trainer which trains the underlying recommenders, the blender which determines the most efficient combination of the recommenders, and the tester for assessing the performance of the system. The enabler incorporates a variety of recommendation algorithms that span from collaborative filtering and content-based techniques to ones based on neural networks. It uses the nested cross validation for automatically selecting the best ML algorithm along with its hyper-parameter values for the given input, according to a specific metric. The enabler can be easily extended to include other recommenders and blenders. The enabler has been extensively evaluated in the context of video-streaming. It outperforms various other algorithms, when tested on the Movielens 1M benchmark dataset.encouraging results. Moreover For example, it achieves an RMSE of 0.8206, compared to the state-of-the-art performance of the AutoRec and SVD, 0.827 and 0.845, respectively. A pilot web-based recommendation system was developed and tested in the production environment of a large telecom operator in Greece. Volunteer customers of the video-streaming service provided by the telecom operator employed the system in the context of an out-in-the-wild field study with a post-analysis of the enabler, using the collected ratings of the pilot, demonstrated that it significantly outperforms several popular recommendation algorithms

A hybrid matchmaking approach in the ambient assisted living domain

During the recent years, several new Information and Communication Technology solutions have been developed in order to meet the increasing needs of elderly with cognitive impairments and support their autonomous living. Most of these solutions follow a human-centred paradigm that aims to provide users with personalised services according to their needs by also ensuring their safety with mechanisms that can automatically trigger appropriate actions in situations where there may be a risk for an elderly. The present paper presents a hybrid matchmaking approach that uses efficiently both a rule-based and a statistical matchmaker in order to (a) propose ambient assisted living services to the end-users, based on their role, status and context of use and (b) identify and resolve problematic cases by automatically selecting the most proper set of services to be called in a single or combined manner

Multiple social network integration framework for recommendation across system domain

A recommender system is a special software that recommends items to a user based on the user’s history. A recommender system comprises users, items and a rating matrix. Rating matrix stores the interactions between users and items. The system faces a variety of problems among which three are the main concerns of this research. These problems are cold start, sparsity, and diversity. Majority of the research use a conventional framework for solving these problems. In a conventional recommender system, user profiles are generated from a single feedback source, whereas, Cross Domain Recommender Systems (CDRS) research relies on more than one source. Recently researchers have started using “Social Network Integration Framework”, that integrates social network as an additional feedback source. Although the existing framework alleviates recommendation problems better than the conventional framework, it still faces limitations. Existing framework is designed only for a single source domain and requires the same user participation in both the source and the target domain. Existing techniques are also designed to integrate knowledge from one social network only. To integrate multiple sources, this research developed a “Multiple Social Network Integration Framework”, that consists of two models and three techniques. Firstly, the Knowledge Generation Model generates interaction matrices from “n” number of source domains. Secondly, the Knowledge Linkage Model links the source domains to the target domain. The outputs of the models are inputs of the techniques. Then multiple techniques were developed to address cold start, sparsity and diversity problem using multiple source networks. Three techniques addressed the cold start problem. These techniques are Multiple Social Network integration with Equal Weights Participation (MSN-EWP), Multiple Social Network integration with Local Adjusted Weights Participation (MSNLAWP) and Multiple Social Network integration with Target Adjusted Weights Participation (MSN-TAWP). Experimental results showed that MSN-TAWP performed best by producing 47% precision improvement over popularity ranking as the baseline technique. For the sparsity problem, Multiple Social Network integration for K Nearest Neighbor identification (MSN-KNN) technique performed at least 30% better in accuracy while decreasing the error rate by 20%. Diversity problem was addressed by two combinations of the cold start and sparsity techniques. These combinations, EWP + MSN-KNN, TAWP + MSN-KNN and TAWP + MSN-KNN outperformed the rest of the diversity combinations by 56% gain in diversity with a precision loss of 1%. In conclusion, the techniques designed for multiple sources outperformed existing techniques for addressing cold start, sparsity and diversity problem. Finally, an extension of multiple social network integration framework for content-based and hybrid recommendation techniques should be considered future work