Analysis of Matric Product Matching Between Cosine Similarity with Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec in PT. Pricebook Digital Indonesia

Consumers today have the option to purchase products from thousands of e-commerce. However, the completeness of the product specifications and taxonomies used to organize products differently in different electronic shop differently. To improve the consumer experience, Pricebook approach for integration of the product through the website to find the cheapest price from various platforms. In our writing, we do approach by using a model of neural language such as TF-IDF (term frequency-inverse document frequency) as well as Word2vec by using the method of cosine similarity. TF-IDF is a way to give the relationship a word weighting (term) against the document. Semantic vector or word embedding is one way to represent the structure of a sentence will be in align with manipulating sentences into vector shapes with Word2Vec. Cosine similarity method is a method to calculate the similarity between two objects that is expressed in two vectors by using keywords (keywords) of a document as the size so that it leads to more products matching good performance and categorization. In addition, we compare the results of the representation of the TF-IDF with Word2vec against a number of the data.

Making Recommendations More Effective Through Framings: Impacts of User- Versus Item-Based Framings on Recommendation Click-Throughs

Companies frequently offer product recommendations to customers, according to various algorithms. This research explores how companies should frame the methods they use to derive their recommendations, in an attempt to maximize click-through rates. Two common framings—user-based and item-based—might describe the same recommendation. User-based framing emphasizes the similarity between customers (e.g., “People who like this also like…”); item-based framing instead emphasizes similarities between products (e.g., “Similar to this item”). Six experiments, including two field experiments within a mobile app, show that framing the same recommendation as user-based (vs. item-based) can increase recommendation click-through rates. The findings suggest that user-based (vs. item-based) framing informs customers that the recommendation is based on not just product matching but also taste matching with other customers. Three theoretically derived and practically relevant boundary conditions related to the recommendation recipient, the products, and other users also offer practical guidance for managers regarding how to leverage recommendation framings to increase recommendation click-throughs.