In the era of online shopping, finding the best deals on products can be a daunting task. With so many online retailers offering similar products at different prices, it can be time-consuming and overwhelming to compare prices across multiple websites manually. That’s where a smart price comparison assistant comes in handy. By utilising AI technology, this assistant can automatically search and compare prices from various online retailers, saving users time and effort. With the ability to analyse data and identify the best deals, a smart price comparison assistant can provide users with a comprehensive and unbiased view of the market. This allows users to make informed purchasing decisions and ensures that they are getting the best value for their money. By utilising AI-driven recommendation engines, this price comparison assistant can not only compare prices but also provide personalised recommendations based on user preferences and previous purchases. By using personalised shopping powered by AI, businesses can offer their customers a unique and tailored experience. This personalised experience leads to higher engagement, loyalty, and ultimately, increased revenue for businesses. In addition to personalised shopping, AIdriven product recommendations also play a significant role in creating engaging and personalised online shopping experiences. With the help of AI-powered personalization tools, the smart price comparison assistant can analyse user behaviour and preferences to personalise search results and provide relevant and accurate product recommendations.

Smart Price Comparison Assistant: Making Shopping Smarter with Machine Learning Techniques

In the burgeoning landscape of e-commerce, the demand for personalized user experiences has become paramount. Traditional methods of online shopping often require users to manually select products for their carts, resulting in a time-consuming and often inaccurate process. In response, this paper presents ShopSculpt, an innovative approach to cart creation that leverages intelligent algorithms to automate product selection based on user interests, profile details, seasonal trends, and weather conditions. Through the integration of predictive modeling and recommendation systems, ShopSculpt aims to streamline the shopping experience, enhancing user satisfaction and engagement. This paper outlines the methodology behind ShopSculpt, presents experimental results demonstrating its efficacy, and discusses its implications for the future of e-commerce. A primary challenge with online shopping is the lack of personalized assistance akin to that found in physical stores. In physical supermarkets, items frequently purchased together are grouped together, encouraging consumers to make additional purchases and thereby boosting sales. We've incorporated this principle into our e-grocery system to create a platform that suggests items to users that they might not have considered previously. Through our efforts, we've established a robust recommendation framework leveraging the Apriori Algorithm for association rule mining and Collaborative Filtering employing the Nearest Neighbors algorithm. We've pinpointed four primary scenarios, encompassing recommendations based on past purchase histories, similarities with other users' preferences, items currently in the user's cart, and top-rated products within the grocery category. This multifaceted approach ensures that our recommendations are tailored to each user's unique needs and preferences. 

Streamlining Grocery Shopping : Personalized Recommendations for Enhanced Cart Performance

This research provides a music recommendation system that creates tailored recommendations for users based on their listening history using a collaborative filtering algorithm and Singular Value Decomposition (SVD). Initially, the research methodology attempted to use cosine similarity to generate recommendations, but it was found to be ineffective due to the inability to handle sparse matrices for large datasets. Therefore, the research shifted its approach to using SVD to overcome this issue. The Amazon Digital Music dataset is used for the implementation of the system, which contains user ratings and reviews for various music products. The dataset is divided into three matrices using the SVD algorithm: the user matrix, the song matrix, and the diagonal matrix. With the use of these matrices, it is possible to forecast missing ratings for unrated products. The predicted ratings are then used to generate personalized recommendations for the user. The Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) metrics are used to gauge the system's performance. According to the evaluation's findings, the system performs admirably in terms of accuracy and efficacy, with low RMSE and MAE values. This indicates that the system can generate accurate recommendations for users based on their listening history, which can enhance the user experience and engagement with music streaming services. In conclusion, the work highlights the effectiveness of the collaborative filtering algorithm with SVD in generating personalized music recommendations for users. The failure of the initial approach using cosine similarity due to the inability to handle sparse matrices for large datasets underscores the importance of selecting appropriate algorithms for specific datasets. The proposed system demonstrates the effectiveness of using SVD for generating accurate and personalized recommendations for users, and future work could explore other machine learning techniques to further improve the system's performance.

Music Recommendation System using Collaborative Filtering with SVD

In the present world from small companies to giant company’s product recommendation system plays a very major role, also people are very much interested in online shopping these days, so Recommendations are utilized to make the customer's job easier and faster. The majority of these recommendations are made based on their previous transaction history and association rules generated from it. Apriori Algorithm is the most widely used method for generating association rules based on frequent product sets. But it has a drawback, while generating association rules it uses the whole transactions list and frequent product sets, ignoring the seasonal transactions for generating rules. Hence seasonal transactions are not considered for mining rules, if a person is buying a cake for Christmas, it’s very likely that recommender system recommends birthday balloons, birthday caps as recommendations. But actually, it should recommend Christmas related products for recommendation. To solve this problem, we proposed a model using chi square test with improvised Apriori algorithm.
This research paper introduces ShopSculpt, an innovative automated shopping cart system designed to enhance the user shopping experience by leveraging intelligent algorithms. ShopSculpt employs a multifaceted approach, considering user shopping interests, patterns, seasonal trends, and current weather conditions to provide personalized and context-aware product recommendations. The aim is to create a dynamic and responsive shopping environment that adapts to individual preferences and external factors, thereby optimizing the user's shopping journey.

ShopSculpt : Crafting Your Cart with Intelligent Ingenuity

Next basket recommendation is a critical task in market basket data analysis. It is particularly important in grocery shopping, where grocery lists are an essential part of shopping habits of many customers. In this work, we first present a new grocery Recommender System available on the MyGroceryTour platform. Our online system uses different traditional machine learning (ML) and deep learning (DL) algorithms, and provides recommendations to users in a real-time manner. It aims to help Canadian customers create their personalized intelligent weekly grocery lists based on their individual purchase histories, weekly specials offered in local stores, and product cost and availability information. We perform clustering analysis to partition given customer profiles into four non-overlapping clusters according to their grocery shopping habits. Then, we conduct computational experiments to compare several traditional ML algorithms and our new DL algorithm based on the use of a gated recurrent unit (GRU)-based recurrent neural network (RNN) architecture. Our DL algorithm can be viewed as an extension of DREAM (Dynamic REcurrent bAsket Model) adapted to multi-class (i.e. multi-store) classification, since a given user can purchase recommended products in different grocery stores in which these products are available. Among traditional ML algorithms, the highest average F-score of 0.516 for the considered data set of 831 customers was obtained using Random Forest, whereas our proposed DL algorithm yielded the average F-score of 0.559 for this data set. The main advantage of the presented Recommender System is that our intelligent recommendation is personalized, since a separate traditional ML or DL model is built for each customer considered. Such a personalized approach allows us to outperform the prediction results provided by general state-of-the-art DL models.

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Intelligent personalized shopping recommendation using clustering and supervised machine learning algorithms

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Intelligent personalized shopping recommendation using clustering and supervised machine learning algorithms