Neo4j Powers Conversational Commerce with eBay ShopBot
eBay is continually looking to improve the ways shoppers search for the items they seek. SVP & Chief Product Officer RJ Pittman explains how existing product searches and recommendation engines are currently unable to provide or infer contextual information within a shopping request. As an example, Pittman considers the information implied within the phrase: “My wife and I are going camping in Lake Tahoe next week, we need a tent.”
He observes that most search engines would react to the word “tent.” But the additional context regarding location, temperature, tent size, scenery, etc. is typically lost. Yet, this type of specific information is actually what informs many buying decisions. Relaying or maintaining this context is often a burden left to the user and a new solution was needed to remove the hard work associated with shopping.
To build the eBay ShopBot, the knowledge graph they needed would be coupled with natural language understanding and artificial intelligence to store, remember and learn from past interactions with shoppers.
eBay chose Neo4j as the native graph database that holds the probabilistic models that aid understanding in the conversational shopping scenario. The Neo4j graph contains both the product catalog and the attributes of shopper interactions while seeking products.
Below is a portion of the knowledge graph eBay Shopbot uses to interpret the customer request to purchase a “brown, leather Coach messenger bag costing less than $100.”
When a shopper searches for “brown bags” for example, eBay ShopBot knows what details to ask about next, such as type, style, brand, budget or size. As it accumulates this information by traversing through the graph, the application is continuously checking inventory for the best match. This is a great example of real-time decision making.
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