PSEUDO-MULTIVARIATE LSTM NEURAL NETWORK APPROACH FOR PURCHASE DAY PREDICTION IN B2B

This research focuses on trying to predict the moment of the next purchase for a customer in vendor-customer B2B scenario using an LSTM neural network and comparing prediction results from different input features. In a previous research we performed predictions for a specific customer product pair and used previous purchases for that pair as input data, but  the number of such previous purchases was often very limited which resulted in low accuracy of predictions. By aggregating purchase data for all products a customer purchased, we were able to get more precise predictions of the next purchase. Additionally, expanding our input feature set yielded even better results. We performed an evaluation of LSTM networks trained with the most successful combination of input features for a six month period. Each of the networks was trained with purchase data up to the starting point of the selected period and the predictions were performed, after which additional input for the following seven days was added to the network. This process was then repeated for the entire six month period and a slight downward trend can be noticed for error metrics, leading to the conclusion that the network would perform even better over time with the addition of future purchases

OSN Model For Business Growth Using Ecommerce Product Recommendation

Now A Days Online Shopping Has Achieved A Tremendous Popularity Within Very Less Amount Of Time. Recently Few Ecommerce Websites Has Been Developed Their Functionalities To A Extent Such That They Recommend The Product For Their Users Referring To The Connectivity Of The Users To The Social Media And Provide Direct Login From Such Social Media Such As Facebook, Twitter, Whatsapp. Recommend The Users That Are Totally New To The Website Client Novel Solution For Cross-Site Cold-Start Product Recommendation That Aims For Recommending Products From E-Commerce Websites. In Specific Propose Learning Both Users And Products Feature Representations From Data Collected From E-Commerce Websites Using Recurrent Top-K To Transform User’s Social Networking Features Into User Embeddings. The Survey Paper Develops A Top-K Approach Which Can Manipulate The Learnt User Implanting For Cold-Start Product Recommendation

An Efficient Cross-Domain Recommendation Technique in Cold-Start Situations

Most of the recent studies on recommender systems are focused on single domain recommendation systems. In the single domain recommendation systems, the items that are used for training and test data set are belongs to within the same domain. Cross-site domains or item recommendations in multi-domain environment are available in Amazon i.e. it incorporate two or more domains. Few research studies are done on the cross-site recommendation systems. Cross-site recommendations provide the relationship between the two sets of items from various domains. They can provide the extra information about the users of a target domain and recommendations will be done based on that. In this paper, we will study cross-site recommendation model on the cold start situation, where the purchase history is not available for the new user. Cold-start is the well-known issue in the area of recommendation systems. It seriously affect the recommendations in the collaborative filtering approaches. 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 across 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 in to another feature representation which can be easier for 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 in to user embedding. Once found, then develop a feature-based matrix factorization approach which can leverage the learnt user embedding for the cold-start product recommendation. Experimental results shows that our approach effectively works and gives the best recommended results in cold start situations

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

Sentimental analysis of audio based customer reviews without textual conversion

The current trends or procedures followed in the customer relation management system (CRM) are based on reviews, mails, and other textual data, gathered in the form of feedback from the customers. Sentiment analysis algorithms are deployed in order to gain polarity results, which can be used to improve customer services. But with evolving technologies, lately reviews or feedbacks are being dominated by audio data. As per literature, the audio contents are being translated to text and sentiments are analyzed using natural processing language techniques. However, these approaches can be time consuming. The proposed work focuses on analyzing the sentiments on the audio data itself without any textual conversion. The basic sentiment analysis polarities are mostly termed as positive, negative, and natural. But the focus is to make use of basic emotions as the base of deciding the polarity. The proposed model uses deep neural network and features such as Mel frequency cepstral coefficients (MFCC), Chroma and Mel Spectrogram on audio-based reviews

REVIEW CLASSIFICATION USING NATURAL LANGUAGE PROCESSING AND DEEP LEARNING

Sentiment Analysis is an ongoing research in the field of Natural Language Processing (NLP). In this project, I will evaluate my testing against an Amazon Reviews Dataset, which contains more than 100 thousand reviews from customers. This project classifies the reviews using three methods – using a sentiment score by comparing the words of the reviews based on every positive and negative word that appears in the text with the Opinion Lexicon dataset, by considering the text’s variating sentiment polarity scores with a Python library called TextBlob, and with the help of neural network training. I have created a neural network model that learns from the review stars and then compare the neural network’s performance against both the Opinion Lexicon and TextBlob’s classification methods. We see that the accuracy of the Opinion Lexicon classification method is 64.38% while the accuracy with TextBlob’s classification method is 65.71% and the neural network model achieves an accuracy of 96.46%. The model would help brands for future reviews left by customers by classifying them as positive, negative, or neutral