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In the age of information overload, customers are overwhelmed with the number of products available for sale. Search engines try to overcome this issue by filtering relevant items to the users’ queries. Traditional search engines rely on the exact match of terms in the query and product meta-data. Recently, deep learning-based approaches grabbed more attention by outperforming traditional methods in many circumstances. In this work, we involve the power of embeddings to solve the challenging task of optimizing product search engines in e-commerce. This work proposes an e-commerce product search engine based on a similarity metric that works on top of query and product embeddings. Two pre-trained word embedding models were tested, the first representing a category of models that generate fixed embeddings and a second representing a newer category of models that generate context-aware embeddings. Furthermore, a re-ranking step was performed by incorporating a list of quality indicators that reflects the utility of the product to the customer as inputs to well-known ranking methods. To prove the reliability of the approach, the Amazon reviews dataset was used for experimentation. The results demonstrated the effectiveness of context-aware embeddings in retrieving relevant products and the quality indicators in ranking high-quality products.

This paper draws a systematic literature review about the use of Machine learning based recommender systems for crop selection, with respect to the PRISMA protocol for systematic reviews. The second section, describes an overview of existing recommender systems in literature. The outline of this study is explained, as well as the method of content analysis used in this article to sort out the papers is introduced in the third section. In the fourth section, the selection process and the literature review matrix are provided. Additionally, the evolvement of research on crop recommendation over the years is considered, a detailed study of the main input features is done. Further, the current challenges found in crop recommendation are listed. The fifth section discusses the outcomes of the study. The last section recapitulates the outcomes of the study, shedding the light of research perspectives worthwhile pursuing.

Crop selection (CS) is one of the most critical elements that affects the final yield directly. As a result, selecting an appropriate crop is always a critical decision that a farmer must make, considering environmental factors. Choosing an appropriate crop for a given farm is a difficult decision including a plethora of variables that influence the final yield. Experts are frequently consulted to assist farmers with CS; but, as this alternative is time consuming and expensive, it is not available to many farms. The use of recommender systems (RSs) in agricultural management has recently brought some captivating and promising results. We propose a systematic literature review (SLR) in this article to find and provide the most relevant and high-quality publications ad- dressing the crop recommendation (CR) question. The core concept of this SLR is inspired from the guidelines of PRISMA 2020.The different CR approaches are discussed, as well as all the most important input features for recommendation, which are determined and classified. We also identified some of the biggest hurdles to using CR in agriculture. Besides, we made an inventory of the most used techniques for CR. Further, we made an inventory of evaluation criteria and evaluation approaches.

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