Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising
Table 5: Comparison of ad cold-start methods
['[BOLD] Method', '[BOLD] Average', '[BOLD] STD']
To evaluate different algorithms for cold start ad embeddings we conducted the following experiment. We used search2vecdwell,in context vectors of queries to construct ad content vectors based on available textual information: title, description, display URL and bid term. n=1,.. ,10 4) CRF phrases, which uses vectors of phrases obtained by CRF segmentation anchor words, which is the Algorithm 1 that uses words instead of phrases. In all above-mentioned algorithms, except anchor algorithms, a hand curated collection of stopwords (e.g., “free shipping" and “best prices") were dropped to improve performance. The performance was measured in terms of cosine similarity between the ad context vector and its corresponding content vector. High similarity indicates that the cold-start method can produce a vector that is close enough to the one that would be learned from sessions, and could therefore be matched to relevant queries in the embedding space. We can observe that anchor phrases, which automatically discards uninformative phrases, outperformed the competing baselines by a large margin.