Recommendations from cold starts in big data This paper examines the challenging problem of new user cold starts in subset labelled and extremely sparsely labelled big data. We introduce a new Isle of Wight Supply Chain (IWSC) dataset demonstrating these characteristics. We also introduce a new technique addressing these challenges, the Transitive Semantic Relationships (TSR) model, which infers potential relationships from user and item text content and few labelled examples. We perform both implicit and explicit evaluation of TSR as a recommender system and from new user cold starts we achieve a hitrate10 of 77% on a collection of 630 items with only 376 supplychain consumer labels, and 67% with only 142 supplychain supplier labels, demonstrating a high level of performance even with extremely few labels in challenging coldstart scenarios. TSR is suitable for any dataset featuring few labels and user and item content, where similarity of content indicates similar relationship forming capability. TSR can be used as a standalone recommender system or to complement existing highperformance recommender models that require more labels or do not support cold starts.

Recommendation generation using personalized weight of metapaths in heterogeneous information networks Abstract   In todayu0027s era of electronic markets with information overload, generating personalized recommendations for ecommerce users is a challenging and interesting problem. Recommending topN items of interest to ecommerce users is more challenging using binary implicit feedback. The training data is usually highly sparse and has binary values capturing a useru0027s action or inaction. Due to the sparseness of data and lack of explicit user preferences, neighborhoodbased and modelbased approaches may not be effective to generate accurate recommendations. Of late, networkbased item recommendation methods, which utilize item related metainformation, have started getting attention. In this work, we propose a heterogeneous information networkbased recommendation model called HeteroPRS for personalized topN recommendations using binary implicit feedback. To utilize the potential of metainformation related to items, we use the concept of metapath. To improve the effectiveness of the recommendations, the popularity of items and interest of users are leveraged simultaneously. Personalized weight learning of various metapaths in the network is performed to determine the intrinsic interests of users from the binary implicit feedback. The proposed model is experimentally evaluated and compared with various recommendation techniques for implicit feedback using realworld datasets, and the results show the effectiveness of the proposed model.