A Novel Cross-Site Product Recommendation Method in Cold Start Circumstances

In the last 20 years, more than 250 research articles were published about research paper recommender systems. In the recent years, the farthest point between internet business applications such as e-commerce websites and social networking applications has interpersonal communication and it has turned out to be progressively obscured. Numerous e-commerce web and mobile applications allowing social logging mechanism where their clients can signing in their websites using their personal social network identities such as twitter or Facebook accounts etc. users can likewise post their recently purchased items on social networking websites with the appropriate links to the e-commerce product web pages. In this paper, we propose a new solution to recommend products from e-commerce websites to users at social networking sites. a noteworthy issue is how to leverage knowledge from social networking websites when there is no purchase history for a customer, especially in cold start situations.in particular, we proposed the solution for cold start recommendation by linking the users to social networking sites and e-commerce websites i.e. customers who have social network identities and have purchased on e-commerce websites as a bridge to map user?s social networking features into another feature representation which can be easier for a product recommendation. Here we propose to learn by using recurrent neural networks both user?s and product?s feature representations called user embedding and product embedding from the data collected from e-commerce website and then apply a modified gradient boosting trees method to transform user?s social networking features into user embedding. Once found, then develop a feature-based matrix factorization approach which can leverage the learned user embedding for the cold-start product recommendation. Experimental results show that our approach effectively works and gives the best-recommended results in cold start situations

Product Knowledge Graph Embedding for E-commerce

In this paper, we propose a new product knowledge graph (PKG) embedding approach for learning the intrinsic product relations as product knowledge for e-commerce. We define the key entities and summarize the pivotal product relations that are critical for general e-commerce applications including marketing, advertisement, search ranking and recommendation. We first provide a comprehensive comparison between PKG and ordinary knowledge graph (KG) and then illustrate why KG embedding methods are not suitable for PKG learning. We construct a self-attention-enhanced distributed representation learning model for learning PKG embeddings from raw customer activity data in an end-to-end fashion. We design an effective multi-task learning schema to fully leverage the multi-modal e-commerce data. The Poincare embedding is also employed to handle complex entity structures. We use a real-world dataset from grocery.walmart.com to evaluate the performances on knowledge completion, search ranking and recommendation. The proposed approach compares favourably to baselines in knowledge completion and downstream tasks

Convolutional-NN and Word Embedding for Making an Effective Product Recommendation Based on Enhanced Contextual Understanding of a Product Review

E-commerce is one of the most popular service applications in the world in the last decade. It has become a revolutionary model from traditional shopping transaction to entire internet commerce. E-commerce needs essential artificial intelligence (AI) to provide the customer with information about a product, called a recommendation machine. Collaborative filtering is a model of a recommendation algorithm that relies on rating as the fundamental calculation to make a recommendation. It has been successfully implemented in e-commerce. Even so, this model has a weakness in sparse product data in which the rating number is very low or sparse. Mostly, only less than 3% of the total user population rate a product, leading to the rise of sparse data. A text sentence document is a part of customers’ feedback that can be converted into a product rating.  According to a traditional approach, bag of word and lexicon model are ignored in a contextual approach. This experiment, it developed a new model to increase the contextuality of text sentences, leading to a more effective rating prediction. We employed a kind of convolutional neural network to generate item latent factor vectors that could be incorporated with probabilistic matrix factorization to make rating prediction. Our method outperformed several previous works based on a metric evaluation using the Root Mean Squared Error (RMSE). In this experiment, we analyzed MovieLens and IMDB datasets, which contained a movie product review

A Hybrid Recommendation System Based on Association Rules

Recommendation systems are widely used in e-commerce applications. Theengine of a current recommendation system recommends items to a particular user based on user preferences and previous high ratings. Various recommendation schemes such as collaborative filtering and content-based approaches are used to build a recommendation system. Most of current recommendation systems were developed to fit a certain domain such as books, articles, and movies. We propose a hybrid framework recommendation system to be applied on two dimensional spaces (User × Item) with a large number of users and a small number of items. Moreover, our proposed framework makes use of both favorite and non-favorite items of a particular user. The proposed framework is built upon the integration of association rules mining and the content-based approach. The results of experiments show that our proposed framework can provide accurate recommendations to users

Hybrid Recommendation System Using Clustering and Collaborative Filtering

A recommendation system is a way of suggesting users a subset of possible choice from a set of choices. An example of recommendation system in e-commerce web applications is to recommend customers what they might like to purchase from a wide variety of items based on their recent purchase behavior or search history. Recommender systems are redefining their meaning in today’s business oriented world by providing organizations an effective means of driving revenue. There are a lot of recommendation engines present at the moment. Some of them work solely on the user’s perspective whereas some work in generalized manner. There is a need that these recommender system start providing users with out of the box choices. They should not be limited to one’s individuality, rather other user preferences based on similarity should also be considered. Hence recommender systems play a major role in e-commerce domains and helps the business to achieve data intelligence. The proposed system explains various methods by which the user can be provided recommendations

Context-aware food recommendation system

Recommendation systems are commonly used in websites with large datasets, frequently used in e-commerce or multimedia streaming services. These systems effectively help users in the task of finding items of their interest, while also being helpful from the perspective of the service or product provider. However, successful applications to other domains are less common, and the number of personalized food recommendation systems is surprisingly small although this particular domain could benefit significantly from recommendation knowledge. This work proposes a contextaware food recommendation system for well-being care applications, using mobile devices, beacons, medical records and a recommender engine. Users passing near a food place receives food recommendation based on available offers order by appropriate foods for everyone’s health at the table in real time. We also use a new robust recipe recommendation method based on matrix factorization and feature engineering, both supported by contextual information and statistical aggregation of information from users and items. The results got from the application of this method to three heterogeneous datasets of recipe’s user ratings, showed that gains are achieved regarding recommendation performance independently of the dataset size, the items textual properties or even the rating values distribution.info:eu-repo/semantics/publishedVersio