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The objective of this assignment is to design an object-oriented system and gain implementation experience in C++ while using classes
INSTRUCTIONS TO CANDIDATESANSWER ALL QUESTIONS
1 Assignment Goals
The objective of this assignment is to design an object-oriented system and gain implementation experience in C++ while using classes, standard data structures and unique C++ properties such as the “Rule of 5”. You will learn how to handle memory in C++ and avoid memory leaks. The resulting program must be as efficient as possible.
2 Assignment Definition
In this assignment you will write a C++ program that simulates a new streaming service - SPLFLIX. SPLFLIX offers users two types of streaming content – movies and tv-episodes, which belong to a given tv-series. Like most streaming services we allow a creation of multiple users, while each user receives user- specific recommendations, according to his watch history However, on SPLFLIX, unlike current treaming services, we give our users the ability to choose which recommendation algorithm to use, out of a number of algorithms
we offer. Each user stores its own watch history, and can use a different recommendation algorithm. After the user chooses to watch a content, a recommendation can be given to the user for the next content.
At each point during the session, there is only one active user (while other users are considered as non-active), which means- only one user can watch SPLFLIX at any given point.
The program will receive a config file (json) as an input, which includes all the information about the available streaming content (see section 3.5).
a. The Program flow
The program receives the path of the config file as the first command line argument. Once the program starts (which should trigger the start() function), it opens the SPLFLIX by printing “SPLFLIX is now on!” to the screen, and initializing a default watching user. The default user
has the name "default", and its preferred recommendation algorithm is the “Length recommender” (as described in section 3.3).
Afterwards, the program enters a loop, in which it wats for the user to enter an action to execute. After each executed action, it should wait for the next action.
The loop ends when the user enters the action "exit". (See actions in part 3.4)
An important note we will test your program on various settings, make sure that in your implementation, the session can be started again after the user enters the exit command. That is – the exit() command should only exit the main loop, and shouldn't change any of your other data structures. If one activates the start() method again after the user entered the exit command – the expected behavior is that the session would resume running.
b. Classes
Session – This class holds all the information that is relevant to a session: a list of users, currently active user, available watching content, and history of all actions (actions log).
Watchable – This abstract class represents a watchable content. Each watchable content has an id, a name, length, and a list of tags that describes it. It has the pure virtual method getNextWatchable(const Session&), which returns a pointer to the watchable content to be recommended to the user, or a null pointer, in the case in which there is no recommendation.
Movie – Inherits from Watchable. This class represents a standalone movie.
Episode – Inherits from Watchable. This class represents an episode of a tv series.
User – This is an abstract class for the different user classes. There are several types of users, which differs by their preferred recommendation algorithms. Each class that is derived from this class implements a different recommendation algorithm (as described in section 3.3). This class stores the user’s name, and his watch history. It has a pure virtual function getRecommendation(), which returns the recommended movie/episode, according to its recommendation algorithm.
BaseAction – This is an abstract class for the different action classes. It has the pure virtual method act(Session& sess) which receives a reference to the current session as a parameter
and performs an action on it, a pure virtual method toString() which returns a string representation of the action, and a flag status, which stores the current status of the action: “PENDING” for actions that weren't performed yet, “COMPLETED” for successfully completed actions, and “ERROR” for actions which couldn't be completed.
After each action is performed, its status should be updated: if the action was completed successfully, the protected method complete() should be called in order to change the status to “completed”. If the action resulted in an error, then the protected method error(std::string& errorMsg) should be called in order to change the status to “error” and update the error message.
When an action results in an error, the program should print to the screen: “Error - <error_message>”
More details about the actions will be provided in section 3.4.
c. Recommendation Algorithms
All algorithms should apply the following common behavior:
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After watching a movie, the next recommendation should be dictated by the user's recommendation algorithm (which could be either a movie or an episode).
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After watching an episode, the next recommendation should be the next episode in the series. If there is no such episode, the recommendation should be dictated by the user’s recommendation algorithm (which could be either a movie or an episode).
Algorithm types:
Length Recommender – This algorithm is based on the assumption that users prefer to watch content in a similar length. It will recommend the content whose length is closest to the average length of the content in the user's watch history, and which isn't in the user's watch history. (3-letter code – len)
Rerun Recommender – This algorithm is intended for users who don't like new stuff. It will recommend content based on this user's watching history. Starting from the first watchable content, in a cyclic order. That is – the first watchable to be recommended is the first watchable watched by the user. Afterwards, if the last watchable that was recommended by the algorithm was at index 𝑖 in the history, and the history is of length 𝑛 the next content to be recommended is at index (𝑖 + 1)𝑚𝑜𝑑 𝑛 .(3-letter code – rer)
Similar Genre – This algorithm will recommend content based on the most popular tag in the user's watch history. If a set of tags has the same popularity, the algorithm will use lexicographic order to pick between them. It will recommend a content which is tagged by the most popular tag in the user's history, which wasn't already watched by this user. If no such content exists, it will try with the second most popular tag, and so on. (3-letter code – gen)
Notes:
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All algorithm rules are applied on all watchable content (movies and episodes together).
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In the case in which there is more than one content which fits the recommendation criteria – for example, two movies whose length is equal to the average content length in the user’s watching history, the content with the smaller index in the content vector would be picked.
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If no content fits the recommendation criteria – a null pointer should be returned.
d. Actions
Below is the list of all actions that can be requested by the user. Each action should be implemented as a class derived from the class BaseAction.
-
Create User – Creates a new user in the current session. Syntax: createuser <user_name> <recommendation_algorithm>
Where the <recommendation_algorithm> is the 3-letter code for that algorithm (as described in section 3.3).
If the 3-letter code is invalid, or there is already a user with that name, this action should result in an error.
Example:
" createuser Yossi len" – Will create a new user instance, with the name Yossi, and its recommendation algorithm will be the Length Recommender.
-
Change Active User– Changes the current active user. Syntax: changeuser <user_name>
If the user doesn't exist, this action should result in an error. Example:
" changeuser Yossi" – Changes the current active user to be Yossi's.
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Delete User – Removes a given user. Syntax: deleteuser <user_name>
If the user doesn't exist, this action should result in an error. Example:
"deleteuser Yossi" – Deletes Yossi's user.
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Duplicate User – Creates a copy of the given user, with its watch history and recommendation algorithm, but with a new
Syntax: dupuser <original_user_name> <new_user_name>
If the original user doesn't exist, or the new user name is already taken, this action should result in an error.
Example:
"dupuser Yossi David" – Creates a copy of Yossi's user, with the name David.
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Print Content List – Prints all the watchable content available. Syntax: content
Each watchable content should be printed in its own line in the following manner:
<content_id> <content_name> <content_length> minutes [<tag_1>, <tag_2>, …,
<tag_n>]
Where <content_name> is given by the content's tostring() method, and <content_id> is the location of the given content in the content vector.
|
Example: "content" will print:
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Print Watch History – Prints the watch history of the current active user. Syntax: watchhist
The first line to be printed should be:
Watch history for <user_name>
Where <user_name> is the name of the current active user.
After the first line, every watchable content in the history of this user should be printed in its own line in the following manner:
<id> <content_name>
Where <content_name> is given by the content's tostring() method, and <id> is the index in the history vector, where 1 is the earliest watchable item.
Example:
"watchhist" will print:
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Watch – Watches a Syntax: watch <content_id>
Where <content_id> is the id of the content in the content vector. After this command, the line:
"Watching <content_name>"
should be printed, where <content_name> is given by the content's toString() method. After watching, the content should be added to the watch history of the current user, and a recommendation for a new content should be given to the user, by printing the following line to the screen:
“We recommend watching <content_name>, continue watching? [y/n]”
If the user chooses yes, a new watch command should be executed (implicitly) for the recommended content.
Attachments:
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