  Alternative recommender systems are critical for ecommerce companies. They
guide customers to explore a massive product catalog and assist customers to
find the right products among an overwhelming number of options. However, it is
a non-trivial task to recommend alternative products that fit customer needs.
In this paper, we use both textual product information (e.g. product titles and
descriptions) and customer behavior data to recommend alternative products. Our
results show that the coverage of alternative products is significantly
improved in offline evaluations as well as recall and precision. The final A/B
test shows that our algorithm increases the conversion rate by 12 percent in a
statistically significant way. In order to better capture the semantic meaning
of product information, we build a Siamese Network with Bidirectional LSTM to
learn product embeddings. In order to learn a similarity space that better
matches the preference of real customers, we use co-compared data from
historical customer behavior as labels to train the network. In addition, we
use NMSLIB to accelerate the computationally expensive kNN computation for
millions of products so that the alternative recommendation is able to scale
across the entire catalog of a major ecommerce site.
