If you are looking for product-query similarity, you could try using a Word2Vec model. You can train a Word2Vec model on your dataset, and then use the model to find the most similar words for each product title and user query. This should give you a better understanding of the similarity between the two.

You can also try using an embedding-based approach, such as using an embedding layer in a neural network. This would enable you to learn more complex relationships between product titles and user queries.

You could also try using a matrix factorization technique such as Singular Value Decomposition (SVD) or Non-Negative Matrix Factorization (NMF). These methods can help you to identify latent features in your dataset, which can be used to generate better recommendations.

Hope this helps!