Collaborative filtering-based recommendation of online social voting

Abstract

Social voting is becoming the new reason behind social recommendation these days. It helps in providing accurate recommendations with the help of factors like social trust etc. Here we propose Matrix factorization (MF) and nearest neighbor-based recommender systems accommodating the factors of user activities and also compared them with the peer reviewers, to provide a accurate recommendation. Through experiments we realized that the affiliation factors are very much needed for improving the accuracy of the recommender systems. This information helps us to overcome the cold start problem of the recommendation system and also y the analysis this information was much useful to cold users than to heavy users. In our experiments simple neighborhood model outperform the computerized matrix factorization models in the hot voting and non hot voting recommendation. We also proposed a hybrid recommender system producing a top-k recommendation inculcating different single approaches.

Design and Development of Powerful Neuroevolution Based Optimized GNNBiLSTM Model for Consumer Behaviour and Effective Recommendation in Social Networks

“…In the realm of recommender systems (RS), several traditional techniques have been wellestablished, including collaborative filtering RS [16], content-based RS [17], and hybrid RS [18]. However, the advent of advanced Neuroevolution models has [20][21][22].…”

Chatterbot implementation using Transfer Learning and LSTM Encoder-Decoder Architecture

“…LSTM networks are extended RNNs consisting of extended memory cells known as gated cells which allow the inputs to be remembered for a long time [16]. The information in the LSTM gated cell memory may be retained or discarded and can be calculated by the weights / value allocated by the algorithm given, i.e.…”

“…If the forget gate performance is 1, the knowledge is retained in the cell state and is indicated to be forgotten by closer to 0. Input gate chooses to let the forget gate input influence the input at the current time stage and it defines how much of the activation of each device is retained [16]. The proposed framework methodology consists of different steps, such as raw data selection, pre-processing of data, extraction of features and NN preparation.…”