Advertising Popularity Feature Collaborative Recommendation Algorithm Based on Attention-LSTM Model

To accurately predict the click-through rate (CTR) and use it for ad recommendation, we propose a deep attention AD popularity prediction model (DAFCT) based on label recommendation technology and collaborative filtering method, which integrates content features and temporal information. First, we construct an Attention-LSTM model to capture the popularity trends and exploit the temporal information based on users’ feedback; finally, we use the concatenate method to fuse temporal information and content features and design a Deep Attention Popularity Prediction (DAVPP) algorithm to solve DAFCT. We experimentally adjust the weighted composite similarity metric parameters of Query pages and verify the scalability of the algorithm. Experimental results on the KDDCUP2012 dataset show that this model collaborative filtering and recommendation algorithm has better scalability and better recommendation quality. Compared with the Attention-LSTM model and the NFM model, the F1 score of DAFCT is improved by 9.80 and 3.07 percentage points, respectively.

An English Teaching Resource Recommendation System Based on Network Behavior Analysis

The sharing of English teaching resources has always been a concern. In order to further improve the value of different English teaching resources, this paper proposes a resource management system based on an improved collaborative recommendation algorithm. The proposed model can predict user behavior based on deep learning models of graph neural network (GNN) and recurrent neural network (RNN). The graph neural network can capture the hidden state of local user behavior and be used as a preprocessing step. Recurrent neural networks can capture time series information. Therefore, the model is constructed by combining GNN and RNN to obtain the advantages of both. In order to prove the effectiveness of the model, we used CNGrid’s real user behavior dataset in the experiment and finally compared the results with other methods. The different deep learning-based models achieved a precision of up to 88% and outperformed other traditional models. The experimental results show that this new deep learning model has good sharing value.

Stream-based explainable recommendations via blockchain profiling

Explainable recommendations enable users to understand why certain items are suggested and, ultimately, nurture system transparency, trustworthiness, and confidence. Large crowdsourcing recommendation systems ought to crucially promote authenticity and transparency of recommendations. To address such challenge, this paper proposes the use of stream-based explainable recommendations via blockchain profiling. Our contribution relies on chained historical data to improve the quality and transparency of online collaborative recommendation filters – Memory-based and Model-based – using, as use cases, data streamed from two large tourism crowdsourcing platforms, namely Expedia and TripAdvisor. Building historical trust-based models of raters, our method is implemented as an external module and integrated with the collaborative filter through a post-recommendation component. The inter-user trust profiling history, traceability and authenticity are ensured by blockchain, since these profiles are stored as a smart contract in a private Ethereum network. Our empirical evaluation with HotelExpedia and Tripadvisor has consistently shown the positive impact of blockchain-based profiling on the quality (measured as recall) and transparency (determined via explanations) of recommendations.

Hybrid Collaborative Fusion Based Product Recommendation Exploiting Sentiments from Implicit and Explicit Reviews

Product recommendation is an important functionality in online ecommerce systems. The goal of the recommendation system is to recommend products with has higher purchase success ratio. User profile, product purchase history etc. have been used in many works to provide high quality recommendations. Product reviews is one of the important source for personalized recommendation. Typical collaborative recommendation systems are built upon user rating on products. But in many cases, these rating information are inaccurate or not available. There is also a problem of biased reviews decreasing the accuracy of recommendation systems. This work proposes a aspect mining collaborative fusion based recommendation system considering both the implicit and explicit reviews. The sentiments about different aspects mined from reviews are translated to multi-dimensional ratings. These ratings are then fused with user profile and demographic attributes to improve the quality of recommendation. The proposed recommendation system has 3.79% lower RMSE, 4.51% lower MAE and 22% lower MRE compared to most recent collaborative filtering based recommendation system.