Keytalk provides AI-powered recommendations that apply to every industry. Our technology boosts relevancy, coverage, novelty, and serendipity in Search and Recommendation results, delivering a cost-effective solution for user conversion and platform growth with automated operations.
1️⃣ Relevancy — How relevant are the recommendations?
2️⃣ Coverage — What percentage of the user-item space can be recommended?
3️⃣Novelty — How surprising are the recommendations in general?
4️⃣ Serendipity — How surprising are the relevant recommendations?
Unlike conventional systems, Keytalk employs multiple AI and ML algorithms to analyze data sources and provide tailored recommendations based on commonly used phrases and expressions in reviews, such as ‘fairly priced,’ ‘neatly packaged,’ and ‘actually works.’ Each item in your library is meticulously tagged with these semantic phrases, allowing for more accurate and personalized results. For a deeper understanding of Keytalk’s semantic capabilities, please read more about semantics below.
Language Data & Semantics¶
Language data is a rich source of business intelligence, but unfortunately, many businesses struggle to utilize this type of data due to its unstructured nature and the many roadblocks that come with processing it. Keytalk utilizes every thread of user communication left online that may contain relevant or even valuable information about users’ preferences. It extracts the relevant semantics from unstructured language data and uses them as new signals for generating search and recommendation results.
Semantics refers to the association between words that reflects the context, emotions, and sentiments embedded in the word when used by humans. For example, if the word romantic is used in the context of movies, Keytalk will analyze its semantic association to other words such as ‘mushy,’ ‘tragic love,’ ‘like the notebook,’ ‘sensual,’ etc. Training AI in semantics is a complex journey that includes multiple levels of data processing and analysis.