In what ways can AI be employed in e-commerce search engines?
Introduction:
As the e-commerce industry continues to grow, retailers are constantly looking for ways to improve their online shopping experience to attract and retain customers. One way to do this is by utilising artificial intelligence (AI) in search engines, which can greatly enhance the accuracy, relevance, and personalisation of search results. With AI-powered search engines, retailers can provide a more intuitive and efficient search experience for customers, resulting in increased sales and customer loyalty. In this context, this article explores the various ways AI can be employed in retailer e-commerce search engines to improve the online shopping experience.
To achieve this, AI can be leveraged in various ways. One such method is through product enrichment, where AI can for example, predict ad-hoc tags from different sources of data such as product images and descriptions to improve the search accuracy in retrieval layer. Additionally, AI-powered query processing techniques, including query expansion, segmentation, and suggestion, can also improve the accuracy and relevance of search results.
Moreover, AI can be utilised to provide product recommendations to customers by analysing customer behaviour such as purchase history, search behaviour and combine that with some characteristics extracted from the content of the products to build product suggestions for the customers. Similarly, for product ranking, AI can use machine learning algorithms that take into account factors such as relevance, popularity, sales history, seasonality, and customer demographics and behaviours, to provide an optimised ranking of search results.
Product Enrichment:
Enrichment is achieved by analysing and extracting valuable information from various sources of data such as product descriptions, images, and reviews. This extracted data is then added to the product data already available in the search engine. AI techniques are often used in this process to automate and speed up the analysis and extraction of valuable information from the data sources.
One example of enrichment is the generation of ad-hoc tags or attributes that describe the products. Ad-hoc tags can be created using natural language processing techniques to extract information from the product description, such as the product’s brand, colour, material, or size. These tags can then be used to improve the retrieval layer of the search engine, allowing for more accurate and relevant search results.
Enrichment can also be achieved through the development of taxonomies. A taxonomy is a hierarchical classification system that organises products into categories and subcategories based on their attributes. Taxonomies can be built using machine learning techniques to automatically identify patterns in the data and group similar products together. This makes it easier for users to find what they are looking for and improves the accuracy of the search results.
Overall, the enrichment process is essential in search engine systems as it ensures that the product data is accurate, comprehensive, and relevant, resulting in more precise and satisfactory search results for the user.
Query Processing:
Query processing is an essential component of search engine systems that involves analysing a user’s search query and retrieving relevant information from the search index. The process of query processing includes several techniques that help to improve the accuracy and relevance of search results.
One of the most common techniques used in query processing is query expansion. Query expansion is the process of adding related terms or synonyms to a user’s search query to help retrieve more relevant results. This is accomplished by analysing the context of the query and identifying terms that are semantically related to the original search terms. For example, if a user searches for “running shoes,” query expansion may add related terms like “jogging shoes” or “athletic shoes” to the search query.
Another important technique used in query processing is query segmentation. Query segmentation is achieved by analysing the user’s search query and identifying relevant keywords and phrases. These keywords and phrases are then used to search the index for content that matches the user’s search intent. For example, if a user searches for “red running shoes,” query segmentation would break down the query into two keywords, “red” and “running shoes,” and use these keywords to search the index for content that matches these terms. One of the benefits of query segmentation is that it helps to handle misspellings and variations in the user’s search query.
Query suggestion is another technique used in query processing that involves offering suggestions to the user as they type their search query. This helps to improve the user experience by providing relevant suggestions that match the user’s search intent. For example, if a user starts typing “running sh,” the search engine may suggest popular search queries like “running shoes for women” or “best running shoes for beginners.”
Overall, query processing is an essential component of search engine systems as it helps to improve the accuracy and relevance of search results. By analysing a user’s search query and applying techniques like query expansion, query segmentation, and query suggestion, search engines can provide users with more precise and relevant search results that match their search intent.
Product Recommendation:
Product recommendation is an important technique used in search engine systems that involves suggesting products to users based on their interests, behaviour, and preferences. There are several approaches to product recommendation, including collaborative filtering, content-based filtering, and hybrid approaches that combine both techniques.
Collaborative filtering is a technique that involves analysing user behaviour and preferences to identify patterns and similarities among users. This information is then used to suggest products that other users with similar preferences have shown interest in. Collaborative filtering can be further divided into two subcategories: user-based and item-based collaborative filtering.
In user-based collaborative filtering, the search engine identifies users who have similar interests and recommends products that these users have shown interest in. For example, if User A and User B have similar purchase histories and search behaviour, the search engine may recommend products that User B has purchased to User A. This technique relies on the assumption that users who have similar interests and behaviour will continue to have similar interests and behaviour in the future.
Item-based collaborative filtering is a technique used in product recommendation that identifies similar products based on the attributes of each product. These attributes are captured through user interactions such as purchase history or product views. The technique uses the collaboration of user behaviour and preferences to recommend products that match their preferences based on the attributes of the products. This technique relies on the assumption that users who like a particular product will also like other products that have similar attributes or characteristics.
Content-based filtering is another technique used in product recommendation that involves analysing the content of each product and suggesting similar products based on their attributes. For example, if a user is interested in running shoes, the search engine may recommend other running shoes that have similar attributes like size, colour, brand, and style. This technique relies on the assumption that users who like a particular product will also like other products that have similar attributes or characteristics.
Hybrid approaches to product recommendation combine both collaborative filtering and content-based filtering techniques to provide more accurate and relevant recommendations. For example, a search engine may use collaborative filtering to identify users with similar interests and then use content-based filtering to recommend products that are similar to those that these users have shown interest in. This technique takes advantage of the strengths of both techniques, providing more accurate and relevant recommendations than either technique would provide on its own.
Product Ranking:
Product ranking in search engine systems involves using various implicit user signals to order search results based on their relevance to a customer’s query. One of the most important implicit user signals used for product ranking is clicks.
Search engines track the number of times that a particular search result has been clicked on by customers. Products that are clicked on more frequently are assumed to be more relevant to the customer’s query and are therefore ranked higher in search results.
Other implicit user signals that can be used for product ranking include time spent on a product page, product views, and add-to-cart events. Search engines can track these signals to identify products that are most likely to lead to a purchase and rank them higher in search results.
Machine learning algorithms are commonly used to analyse these implicit user signals to improve product ranking. One type of machine learning algorithm that is often used is learning to rank algorithms. These algorithms use a combination of features, such as click-through rates and product attributes, to learn how to rank products in a way that is most likely to result in a purchase.
In some cases, it may be desirable to rank products based on average user signals rather than individual user signals. For example, in situations where there is limited data available for individual users, it may be more effective to use aggregate data to rank products. In these cases, average user signals such as average click-through rates or average time spent on product pages can be used to rank products.
By utilising implicit user signals such as clicks and applying machine learning algorithms, search engine systems can present search results to customers in a way that is most relevant to their query and most likely to lead to a purchase. This can lead to increased customer satisfaction and sales for retailers.
In addition to improving the relevance of search results, product ranking algorithms may also incorporate diversification techniques to ensure that search results are varied and offer a range of products to customers.
Diversification techniques involve balancing relevance with diversity, by selecting search results that are not only relevant to the customer’s query but also diverse in terms of attributes such as brand, price range, and product type. This helps to prevent customers from being presented with too many similar products and encourages them to explore a wider range of products.
One common diversification technique is called “top-k diversification”, where the ranking algorithm selects the top k products based on relevance and then re-ranks them to include a diverse set of products. This can be achieved by adding a diversity component to the ranking algorithm, which ensures that the final search results include a mix of different products.
By incorporating diversification techniques into product ranking algorithms, search engine systems can provide customers with a better shopping experience by presenting them with a range of relevant and diverse products to choose from.
Summary:
AI has transformed the retail industry, particularly in the area of e-commerce search engines. With the rise of big data and the increasing demand for personalised shopping experiences, AI has emerged as a crucial tool for retailers to remain competitive. By analyzing customer data, AI can provide personalised recommendations, relevant search results, and accurate product information. AI can also predict ad-hoc tags, recognize visual characteristics of products, and build taxonomies to structure product information. Additionally, AI can improve the accuracy and relevance of search results through query processing techniques such as query expansion, segmentation, and suggestion. By harnessing these AI techniques in their search engines, retailers can offer a better shopping experience for customers, drive sales, and ultimately achieve a competitive advantage in the market.
