Netflix, YouTube, Amazon, Google- recommender system is everywhere. You name it, and it is there. For example, when you enter a song for search in YouTube, the domain gives you an option ‘Auto-Play’ for the list of recommended songs it has lined up for you. This list is recommended on the basis of a user’s search. Whether a personalization recommender system is a boon or a bane is still a subject of debate among digital experts, but the research paper International Journal of Applied Engineering Research, says, “The recommender system employs the filtering mechanisms to provide the item of interest to the user.” This makes personalization recommender systems an interesting tool to exploit so as to enhance digital experience of your users. It gives the industries a huge scope of income along with a mechanism to outshine competitors.
In the first section of this insight, we shall focus on two major paradigms of the recommender system, i.e., content-based, and collaborative. You can learn more about these systems here.
Major paradigms of recommender systems (Filtering Types)
The comparative table below will give you a peek into the major difference between collaborative filtering mechanisms and content-based methods of the recommender system.
| Collaborative Methods | Content Based Methods |
| In these methods, recommender systems work just on the basis of historical interactions tracked between a user and products so as to create new recommendations. | Content-based approach is a little intricate and it personalizes recommender system in a detailed manner. For example, it may use a user’s personal information like age or sex, or demographics, such as country/ region/ locality to recommend more targeted results. |
| The interactions between a user and the items are stored in a user-item interactions matrix. | In content-based methods, the interactions, instead of being stored, are in fact, observed on the basis of user-item interactions. |
| The argument supporting collaborative methods is that previous user-item interactions suffice the need for detection of similar items, or similar users so as to make predictions, and hence personalize recommendations. | Here, the entire idea revolves around building such models which are based on real-time, available information/ features. For example, females tend to rate certain movies better than males. So the next time, personalized recommender system will just use the profile, such as age and sex, to determine relevance of movie suggestions to this user. |
So which of the two methods is better for businesses? Towards Data Science provides a detailed answer to this question, but by and large hybrid effort is what you need. In real-work, merging and matching techniques of both these methods will lay the foundation for great outputs for business, and excellent DX for users.
That being said, different industries or domains can employ different mechanisms and tools for the best results. This is where we suggest some key characteristics and filtering methods which can be applied for great UX and higher income.
Industry characteristics and filtering type for each
| Industry (and type) | Filtering Type | Characteristics |
| Music (Listening) | Collaborative, Content Based, Context Based | Music repetition, listener’s time, emotion, event, location, Polysemy and Synonymy |
| Video (Watching and listening) | Collaborative, Content-Based, Context Based, Hybrid | Business value, movie sequel, targeted marketing tools, freshness and scaling of streaming |
| Product (e-commerce) | Collaborative, Hybrid |
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| News (Publishing) |
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News locality, time relevance of article, volatility, volume, reading time sequence |
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Regularity in mobility, location, people traveling in groups, mode of travel |
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Collaborative, Content Based, Context Based | Popularity, lifestyle, geolocation, taste, attitude, preferences |
| E-Learning (Informative videos/ tutoring/ e-learning apps/ online coaching) | Content based, Context-based, Hybrid | Ubiquitousness, Age of learner, learning style, learning capacity, knowledge level, memory capacity |
| Restaurant (Food/ Delivery) | Context Based, Hybrid | Time constraint, personal well-being, quality of food, companion, location, novelty, type of food, weather condition |
Table Source: https://www.ripublication.com/ijaer18/ijaerv13n15_100.pdf
RI Publication survey provides much detailed overview regarding adaptation strategies which must be harnessed for creating personalized recommendations for users. The characteristics mentioned in the aforementioned table pertain specifically to industry type/ domain. When employed meticulously, these characteristics can bring out specific recommendations for each user.
For an effective personalization and even more specific recommendation, businesses can employ online recommendation applications and tools. For the right experience at right time for the right audience, custom alternatives need to be created. Industry-specific personalization recommender systems provide accuracy on the basis of personal agents that can be checked through prior research as well as input data.
