Enhancing Product Catalogs with User Insights

Product catalogs are very important for websites that sell things, like Amazon. They use something called knowledge graphs (KGs) to keep track of different items and their attributes. However, these catalogs can sometimes become outdated or incomplete because sellers frequently add new products. This can create challenges in maintaining accurate information which users rely on to search for items.

#What Are Knowledge Graphs?

Knowledge graphs are structured ways to represent information. They consist of triplets that include a subject, predicate, and object. For example, a triplet could be “Marie Curie - birthplace - Warsaw.” This format helps organize information in a way that can be easily searched.

#The Problem with Knowledge Graphs

Although knowledge graphs are widely used, maintaining their accuracy is a big issue. Sellers might introduce new items without providing complete details about them. This means that KGs can end up with missing information, which users depend on for product searches.

To fix this, some methods attempt to automatically fill in missing information. This process is known as Knowledge Graph Completion (KGC). However, one major challenge is that there are a lot of possible combinations of entities and predicates. This makes it difficult to ensure the predictions made are correct.

#The Role of User Queries

User searches provide important feedback on what information is useful. When people search for products, they often provide clues about what attributes are important. For example, if a user searches for “red blouse,” it indicates that color is an important attribute to consider.

By looking at user queries, we can find patterns and insights that help enrich the knowledge graph. This way, we can make predictions that are more relevant to users' needs.

#Our Approach

This paper proposes a method to guide the filling of missing information in product catalogs by using user search logs. By analyzing what users are searching for, we can identify which pieces of information are more relevant and should be prioritized when enriching KGs.

#How It Works

1. Extracting Information from User Queries: We look at the types of searches users are making and extract the relevant entity-predicate pairs from these queries. For instance, if users frequently ask about the birthplace of Marie Curie, then this information can be used to fill the knowledge graph.

2. Guided Prediction: Instead of randomly guessing what information to add, we use the extracted entity-predicate pairs to make guided predictions about missing information. This helps narrow down the focus to what users are really interested in.

3. Testing Performance: We evaluate our method using public knowledge graphs, such as DBPedia and YAGO. Our goal is to demonstrate that using user queries can lead to better predictions in terms of both correctness and relevance.

#Why User Queries Matter

User queries reflect what people are actually looking for, making them a powerful tool for improving knowledge graphs. The questions users ask can reveal gaps in the data that need to be filled. By focusing on these queries, we can significantly reduce the number of irrelevant predictions.

#Results from Experiments

In our evaluations, we compared our approach against traditional methods that don’t use user queries. The results show that using user query guidance led to a substantial increase in the number of correct predictions.

• For example, when comparing our method to another approach that didn’t use user queries, we saw an increase from just a few correct predictions to hundreds. This indicates that our method effectively narrows down the search space for missing information.

#Benefits of Query Guidance

The benefits of using user queries in guiding predictions include:

• Increased Accuracy: By relying on what users are specifically searching for, we can make predictions that are not only correct but also relevant to their needs.

• Improved Efficiency: By filtering out irrelevant predictions, we save time and resources when managing knowledge graphs.

• User-Centric Approach: This method places the user's interests at the forefront, making product searches more intuitive and helpful.

#Limitations

While our approach shows promise, there are some limitations to consider:

• Dependence on Query Logs: The effectiveness of our method relies heavily on the availability of query logs. If logs are incomplete or not representative, the predictions may not be as effective.

• Complexity of User Searches: Not all user searches are straightforward. Some may involve complex inquiries that are harder to analyze and draw insights from.

#Future Work

There is still much to explore in this area. Future research could involve looking at more sophisticated structures that arise from complex queries. Additionally, it may be beneficial to combine various methods of guidance and filtering to further enhance the predictions.

#Conclusion

In conclusion, guiding knowledge graph enrichment with insights from user searches can significantly improve the accuracy and relevance of product catalogs. By focusing on what users are looking for, we can make product searches more effective and user-friendly. As the landscape of online shopping continues to evolve, such methods will be crucial in ensuring that product information remains reliable and relevant.