Notes for my future revision. Recommendation engine need at least to work: 1. Who the target is - personal attributes, OR/AND 2. The preference of the target *Recommendation Engine types and corresponding algorithm types:* 1. Generic -Association rule analysis (eg market basket) 2. Personalised 2a Content-based filtering (using cosine similarity) 2b Collaborative filtering --Model Based --Memory Based ---Item-based CF ---User-based CF 2c Combination of Content-based and Collaborative Filtering **Association Rule Analysis** List of all items in a transaction/basket/cart. Not involving attributes of the products. SUPPORT of an item =Support of Item1 =Chance of an Item1 appearing among all the baskets CONFIDENCE of Item2 given Item1 =Chance of Item2 appearing given Item1 =Frequency of Item2 and Item1 appears in same basket / Frequency of Item1 appearing in a basket =Conditional Probability LIFT of Item 1 and 2 together = Confidence(Item2, Item1) / Support(Item1) =Conditional Prob / Prob of Conditon For every combination of item, calculate their Association Rule, i.e. the SCL values: 1. Support 2. Confidence 3. Lift Example: Given ItemA, SCL with ItemB is xxx Given ItemA, SCL with ItemC is xxx Given ItemA and ItemZ, SCL with ItemB is xxx ...and so on Apriori Algorithm can be used.
@juliusjulius1146
Жыл бұрын
Nice explanation, I love the way you teach with real life examples. Excellent teaching
@spicytuna08
7 ай бұрын
thanks much amar. i see a possible scaling problem. let's say there are 1000 items. associative rule with just two items would involve combination of (1000, 2 ). associative rule with just three items would involve combination of (1000, 3 ). associative rule with just n items would involve combination of (1000, n ). now this amounts to huge set of rules. you explained in case of 2 items. how to build rules in case of N items? again, your videos are outstanding.
@nehasrivastava8927
3 жыл бұрын
finally found the best recommendation systems series..Thanku sir
@UnfoldDataScience
3 жыл бұрын
Welcome Neha.
@rohitreddy3609
3 жыл бұрын
Very neatly explained bro. Love the way you made things simple. Please keep up the good work.
@UnfoldDataScience
3 жыл бұрын
Thanks Rohit, your comments are my motivation.
@pankhurithakur4291
Жыл бұрын
very informative and comprehensive video. Thankyou for google explanation.
@sandipansarkar9211
2 жыл бұрын
finished watching
@chinweezulu4681
10 ай бұрын
Simple and detailed explanation. Weldone👏
@siddhantjain452
2 жыл бұрын
Ek no video !! better than SimpliLearn !!
@UnfoldDataScience
2 жыл бұрын
Thanks Siddhant.
@rajan9382
4 жыл бұрын
Nice explanation, thanks sir. Waiting for your next lectures.
@UnfoldDataScience
4 жыл бұрын
Thanks Rajan. Keep Watching :)
@Dami_San
2 жыл бұрын
Thank You. The explanation was simple and straight forward
@UnfoldDataScience
2 жыл бұрын
Welcome Dami.
@rameshmamilla5392
4 жыл бұрын
Thanks for the good info Aman! waiting for the next video.
@UnfoldDataScience
4 жыл бұрын
Welcome Ramesh :)
@prekshajain9143
Жыл бұрын
Point to point amazing explaination,
@UnfoldDataScience
Жыл бұрын
Thanks Preksha. pls share with friends as well
@vipinkumar-dm2nd
3 жыл бұрын
Hi, Clear and Good presentation skills, Thank you for sharing :-)
@UnfoldDataScience
3 жыл бұрын
Thanks Vipin. Your comments are my motivation.
@pei-yungchang7259
3 жыл бұрын
Thank you for your clear explanation on recommendation system!
@UnfoldDataScience
3 жыл бұрын
Glad it was helpful Pei-Yung.
@user-ng4pp7fp6t
3 ай бұрын
good bhaiyya
@negusuworku2375
Жыл бұрын
Thanks a lot, very good. keep it up
@sandipansarkar9211
3 жыл бұрын
great explanation
@UnfoldDataScience
3 жыл бұрын
Thanks Sandipan.
@faberrocalv8958
4 жыл бұрын
Hi! Thanks for the video, my questions is: What are the recommended values for Support, Confidence and Lift to consider that a rule is strong enough and valid?
@UnfoldDataScience
4 жыл бұрын
Hello, It depends on the domain knowledge. There is no definite rule to fix a value.
@prajwalbharadwaj2386
Жыл бұрын
you have to design a min_sup (Minimum Support) or min_conf (Minimum Confidence) threshold values. The more relaxed the support parameter, the more the candidates you induce into an itemset ; hence more the computation or more passes the apriori algo has to make!!
@educationsoulwelfaresociet6184
3 жыл бұрын
I appreciate your efforts. Please keep up the good work.
@UnfoldDataScience
3 жыл бұрын
I will try my best
@sadhnarai8757
4 жыл бұрын
Verry good aman
@UnfoldDataScience
4 жыл бұрын
Thank you :)
@rupalishirkande6163
2 жыл бұрын
Nice explanation 🤗
@sadhnarai8757
4 жыл бұрын
Very good Aman
@UnfoldDataScience
4 жыл бұрын
Thank you.
@rutujagurav466
3 жыл бұрын
Very nice explanation thank you sir
@UnfoldDataScience
3 жыл бұрын
You're most welcome Rutuja.
@harsh6169
4 жыл бұрын
Nice.. waiting for next coding video on this...Also request if you can upload some videos on Time series forecasting like Arima, Exponential Smoothening, Prophet..etc. Thanks!
@UnfoldDataScience
4 жыл бұрын
Thanks Harsh, yes I plan to create a separate playlist on Time series forecasting and natural language processing as well.
@harsh6169
4 жыл бұрын
@@UnfoldDataScience Thank you so much. Waiting for your playlist.
@preranatiwary7690
4 жыл бұрын
Very nice
@UnfoldDataScience
4 жыл бұрын
Thank you :)
@r21061991
3 жыл бұрын
Great video !!
@kaifali4691
2 жыл бұрын
great explanation..thanks a lot!!
@UnfoldDataScience
2 жыл бұрын
Thanks Kaif
@sahilsangam3846
4 жыл бұрын
Very nice explanation 👍
@UnfoldDataScience
4 жыл бұрын
Thanks for liking Sahil .Keep watching.
@Upmit
2 жыл бұрын
What are Two way and three way lift in Market basket analysis, how can we calculate it.
@adityaverma4770
4 жыл бұрын
your content is good ,thanks
@UnfoldDataScience
4 жыл бұрын
Welcome Aditya. Happy Learning.
@prachitiwari3394
3 жыл бұрын
I am having project on tour and travelling in which I have to analyze impact of sociodemographic factors while selecting different destinations, so in this case which technique I should apply?
@UnfoldDataScience
3 жыл бұрын
You can go for regression models or Random forest.
@r21061991
3 жыл бұрын
@@UnfoldDataScience Can she try boosting methods for this problem ???
@vattypants
2 жыл бұрын
Try me 😉
@merlingrace2884
4 жыл бұрын
What does high support and high confidence mean? Low support high confidence High support low confidence and Low confidence and low support?
@UnfoldDataScience
4 жыл бұрын
HI Merlin, the answer of all your questions can be understood from what is support and confidence. say A= Milk and B=Bread Support = (A+B)/Total number of transactions, means out of total transactions, how may has A+B together. Confidence = (A+B)/A, means out of all the transactions having A how many has B in it.
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