Jinni powers video guides with a fun, intuitive content discovery experience. Our award-winning solution exceeds viewers’ expectations as they easily find content that perfectly fits their tastes and moods – meaning they find and watch more of what they like. The only hybrid discovery solution in the market to combine semantic discovery, metadata analysis and collaborative filtering: Jinni delivers the best of all worlds to enable superior discovery across all catalogs – Live TV, VOD, DVR, and OTT. Jinni matches deep content genetics with a user’s unique Entertainment Personality™ to power any guide with a truly personalized, user-centric experience.


Pay TV operators invest heavily in great content for their subscribers, but most view only a handful of the most popular and promoted content. Customers are frustrated that they “can’t find anything to watch”. Integrating the Jinni semantic discovery solution helps subscribers discover more of the great content already offered and they will enjoy watching.
One personalized guide for all available catalogs (EPG, VOD, PPV) and all screens (TV, tablet, mobile.) Jinni discovery platform enables the ultimate TV everywhere solution for smart guides that take user experience to the next level.


The streaming video market is exploding as more and more consumers watch video over-the-top on their TVs, tablets, PC and smartphone. The always on revolution makes massive amounts of content available to everybody, all the time – but what about user experience? Don’t let your customers get bogged down in information overload. Lead them to all the best on-demand content suited to their personal tastes and mood.

Your guide is your brand. Subscribers should love it.


Jinni provides a complete discovery-and-recommendation engine for video guides delivering content to STBs, Smart TV’s, PCs, tablets, consoles and mobile phones via a comprehensive, easy to integrate set of RESTFUL APIs. Jinni employs multiple engines to enable discovery across EPG, VOD and over-the top catalogs combined.
The service enables content providers and consumer electronics manufacturers to develop a next- generation guide that delivers real personalization and superior user experience. Users easily find more great content they enjoy – driving consumption and reducing churn.
The Business Rules Engine allows Jinni’s customers to fine-tune recommendation sets, promote or exclude content using a rich set of rules, and support business objectives in the discovery process. Business rules are managed using an online Web tool.

For API documentation


Content-based Discovery

Content-Based Discovery includes intuitive explore tools, which do not require personal user identification or consumption data.

Powerful Searchbox – enables classic search (by title, cast, etc.) as well as semantic search using natural phrases.

Browse by Mood – allows viewers to drill down to relevant content within several clicks, each click filters the search by an intuitive category like Mood, Plot, Style or Place. Browse by Mood enables intuitive discovery beyond the traditional genre.


Similar Titles – provides movie and TV show recommendations based on similarity. Similarity is determined by semantic content attributes such as similar Mood, Plot and Style. Metadata and statistics may be applied in certain contexts. The “Why Similar” explanation increases user’s trust in the recommendation and explains the reason for presenting a similar title.



Cross Catalog Recommendations – users may search across all catalogs within a single search or search a specific catalog: Live TV, VOD, DVR, or OTT.

Genome Information – Jinni’s customers can present descriptive gene tags providing meaningful information about the elements of the movie or TV show including title, mood, plot and style. Genome information provides transparency and is proven to increase viewer’s trust in the product. On Tablets and PC tags can also be used to seed a new, contextual search.

Personalized Discovery

Personalized discovery features are available to registered users and transform the service into a personal entertainment hub, tailored for individual tastes and moods.

Unique taste profile and taste based recommendations - based on the understanding that users are diverse and there is no such thing as “average taste”, Jinni analyses the user’s consumption behaviour and creates a unique taste-profile (“Entertainment Personality”) for each individual or household using the service. A taste profile will usually include multiple tastes to reflect versatile viewing behavior and changing moods. User tastes are defined using the same intuitive, semantic tags used to classify the content. The Taste Profile is generated by collecting explicit user input like ratings, and implicit user input including DVR recordings, VOD purchases and channel surfing. Each of the inputs is weighted by relevance, and affects the Entertainment Personality accordingly.



Personalized Guide – Jinni allows creation of a personalized version of the Guide highlighting the shows within the Guide which best fit the user according to taste-profile. The Guide can also be condensed to display only the shows that fit the user’s taste profile. This feature caters to viewers who would like to remain within the familiar Guide experience while enjoying personalized recommendations.

Why Recommended for You” Explanation - recommendations are accompanied by natural language explanations for the reasons a title was recommended. The transparent explanations create users’ trust in the guide and in the recommendations and increase consumption and loyalty.

Your Match - a rank indicating, per title, how closely the title matches the user’s taste. Your match is a simple yet very useful personal indicator to help users select the right content while exploring the VOD library, the program guide or during channel surfing.

Social Discovery

Jinni provides effective social features, transcending the traditional buzz and trend features offered by mainstream social TV applications. Jinni’s social features cut through the noise and leverage the social graph to expose relevant social information and improve discovery.

Watch Together - Jinni is capable of providing effective group recommendations. For a group of viewers in the household, friends on the social network or for any given group, Jinni will recommend content that best fits the whole group by crossing the group’s entertainment personalities into an optimized recommendation.


Taste Buddies Recommendations - Jinni expands the social sphere beyond the user’s existing network. In a similar fashion to Social Recommendations, Jinni allows users who are not active on social networks to enjoy high quality social recommendations by Taste Buddies: users sharing similar tastes across the operator network.

“Why Close To You” - following the explainability and transparency principles Jinni provides descriptive text with the reasons certain friends were highlighted as taste neighbours .

For API documentation