Recommendation of Smart Devices Using Collaborative Filter Approach

Abstract

Consumers now have more options because to the growth of e-commerce, but there is also an abundance of information. Users are looking for technologies that will allow websites to automatically present goods that they may be interested in so they may swiftly locate preferred products from enormous resources. In order to automate the suggestion process, recommender systems are developed. The accuracy criterion of the suggestion in the area of collaborative filtering. One algorithm's implementation is never simple or straightforward due to an algorithm. We suggest a slope one technique that may be used in various recommender systems to address these issues. It is based on the combination of reliable data and user similarity. Three methods make up this algorithm. We should choose reliable facts first. The similarity between users should be calculated second. Third, we must the final suggestion is obtained by weighting this similarity with the modified slope one method equation. Using the [1] Kaggle dataset, we conducted a number of trials, and the findings show that our recommender superior to the conventional slope one method in terms of performance.